EVALUACIÓN DE MODELOS

1. Importación y estandarización de la información

load("../data/Frecuencia_De_Accidentes_Diario.Rda")
load("../data/Dias_Especiales_Diario.Rda")

Se crean las columnas de accidentes Graves y leves para saber la frecuencia por día

library(reshape)
## 
## Attaching package: 'reshape'
## The following object is masked from 'package:dplyr':
## 
##     rename
## The following objects are masked from 'package:tidyr':
## 
##     expand, smiths
Total_Dataset_Freq <- cast(Total_Dataset_Freq[,c(1,2,3)],FECHA~GRAVEDAD)
## Using FREQ as value column.  Use the value argument to cast to override this choice

Se agrega la columna TOTAL_ACCIDENTES

Total_Dataset_Freq$TOTAL_ACCIDENTES <- Total_Dataset_Freq$ACCIDENTES_GRAVES + Total_Dataset_Freq$ACCIDENTES_LEVES
Total_Dataset_Freq <- sqldf("SELECT * 
              FROM Total_Dataset_Freq
              LEFT JOIN Dias_Especiales USING(FECHA)")
Total_Dataset_Freq$DIA <-as.factor(format(Total_Dataset_Freq$FECHA,'%u'))
save(Total_Dataset_Freq,file="../Modelos/Total_Dataset_Freq_diaria.Rda")

2. Partición de los datos para entrenamiento y test

Se ajustarán modelos con la información disponible desde el 01 de enero de 2014 hasta el 31 de diciembre de 2017 y se utilizará el año 2018 para validar el modelo:

Train_D_Dataset <- subset(Total_Dataset_Freq, ANO!="2018")
summary(Train_D_Dataset$ANO)
## 2014 2015 2016 2017 2018 2019 2020 2021 
##  365  365  366  365    0    0    0    0

Se ajustan otra vez los niveles del factor ANO

Train_D_Dataset$ANO <- factor(Train_D_Dataset$ANO)
summary(Train_D_Dataset$ANO)
## 2014 2015 2016 2017 
##  365  365  366  365
library(sqldf)
Test_D_Dataset <- sqldf("SELECT *  
       FROM Total_Dataset_Freq
       WHERE ANO == 2018")
summary(Test_D_Dataset$ANO)
## 2014 2015 2016 2017 2018 2019 2020 2021 
##    0    0    0    0  365    0    0    0

Se ajustan otra vez los niveles del factor ANO

Test_D_Dataset$ANO <- factor(Test_D_Dataset$ANO)
summary(Test_D_Dataset$ANO)
## 2018 
##  365

3. Selección de las mejores variables para el modelo

Se utilizará el método forward selection para elegir las mejores variables explicativas del modelo teniendo como criterio aquellas variables que presente mejor R^2 ajustado

Selección de variables para Total accidentes

library (leaps)
regfit.fwd=regsubsets (TOTAL_ACCIDENTES∼ANO+MES+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Previo_feriado+Prima+Mujer+Padre+Madre+AmoryAmistad+Semana_Santa+Viernes_Antes_Puente+Quincena+Viernes_Desp_Quincena_v1+Viernes_Desp_Quincena_v2+Feria_Flores,Train_D_Dataset, method ="forward", nvmax= 80)
## Warning in leaps.setup(x, y, wt = wt, nbest = nbest, nvmax = nvmax,
## force.in = force.in, : 1 linear dependencies found
## Reordering variables and trying again:
summary (regfit.fwd)
## Subset selection object
## Call: regsubsets.formula(TOTAL_ACCIDENTES ~ ANO + MES + DIA + SEMANA + 
##     Feriado_Lunes + Feriado_Otro + Previo_feriado + Prima + Mujer + 
##     Padre + Madre + AmoryAmistad + Semana_Santa + Viernes_Antes_Puente + 
##     Quincena + Viernes_Desp_Quincena_v1 + Viernes_Desp_Quincena_v2 + 
##     Feria_Flores, Train_D_Dataset, method = "forward", nvmax = 80)
## 86 Variables  (and intercept)
##                          Forced in Forced out
## ANO2015                      FALSE      FALSE
## ANO2016                      FALSE      FALSE
## ANO2017                      FALSE      FALSE
## MES02                        FALSE      FALSE
## MES03                        FALSE      FALSE
## MES04                        FALSE      FALSE
## MES05                        FALSE      FALSE
## MES06                        FALSE      FALSE
## MES07                        FALSE      FALSE
## MES08                        FALSE      FALSE
## MES09                        FALSE      FALSE
## MES10                        FALSE      FALSE
## MES11                        FALSE      FALSE
## MES12                        FALSE      FALSE
## DIA2                         FALSE      FALSE
## DIA3                         FALSE      FALSE
## DIA4                         FALSE      FALSE
## DIA5                         FALSE      FALSE
## DIA6                         FALSE      FALSE
## DIA7                         FALSE      FALSE
## SEMANA02                     FALSE      FALSE
## SEMANA03                     FALSE      FALSE
## SEMANA04                     FALSE      FALSE
## SEMANA05                     FALSE      FALSE
## SEMANA06                     FALSE      FALSE
## SEMANA07                     FALSE      FALSE
## SEMANA08                     FALSE      FALSE
## SEMANA09                     FALSE      FALSE
## SEMANA10                     FALSE      FALSE
## SEMANA11                     FALSE      FALSE
## SEMANA12                     FALSE      FALSE
## SEMANA13                     FALSE      FALSE
## SEMANA14                     FALSE      FALSE
## SEMANA15                     FALSE      FALSE
## SEMANA16                     FALSE      FALSE
## SEMANA17                     FALSE      FALSE
## SEMANA18                     FALSE      FALSE
## SEMANA19                     FALSE      FALSE
## SEMANA20                     FALSE      FALSE
## SEMANA21                     FALSE      FALSE
## SEMANA22                     FALSE      FALSE
## SEMANA23                     FALSE      FALSE
## SEMANA24                     FALSE      FALSE
## SEMANA25                     FALSE      FALSE
## SEMANA26                     FALSE      FALSE
## SEMANA27                     FALSE      FALSE
## SEMANA28                     FALSE      FALSE
## SEMANA29                     FALSE      FALSE
## SEMANA30                     FALSE      FALSE
## SEMANA31                     FALSE      FALSE
## SEMANA32                     FALSE      FALSE
## SEMANA33                     FALSE      FALSE
## SEMANA34                     FALSE      FALSE
## SEMANA35                     FALSE      FALSE
## SEMANA36                     FALSE      FALSE
## SEMANA37                     FALSE      FALSE
## SEMANA38                     FALSE      FALSE
## SEMANA39                     FALSE      FALSE
## SEMANA40                     FALSE      FALSE
## SEMANA41                     FALSE      FALSE
## SEMANA42                     FALSE      FALSE
## SEMANA43                     FALSE      FALSE
## SEMANA44                     FALSE      FALSE
## SEMANA45                     FALSE      FALSE
## SEMANA46                     FALSE      FALSE
## SEMANA47                     FALSE      FALSE
## SEMANA48                     FALSE      FALSE
## SEMANA49                     FALSE      FALSE
## SEMANA50                     FALSE      FALSE
## SEMANA51                     FALSE      FALSE
## SEMANA52                     FALSE      FALSE
## SEMANA53                     FALSE      FALSE
## Feriado_Lunes                FALSE      FALSE
## Feriado_Otro                 FALSE      FALSE
## Previo_feriado               FALSE      FALSE
## Mujer                        FALSE      FALSE
## Padre                        FALSE      FALSE
## Madre                        FALSE      FALSE
## AmoryAmistad                 FALSE      FALSE
## Semana_Santa                 FALSE      FALSE
## Viernes_Antes_Puente         FALSE      FALSE
## Quincena                     FALSE      FALSE
## Viernes_Desp_Quincena_v1     FALSE      FALSE
## Viernes_Desp_Quincena_v2     FALSE      FALSE
## Feria_Flores                 FALSE      FALSE
## Prima                        FALSE      FALSE
## 1 subsets of each size up to 81
## Selection Algorithm: forward
##           ANO2015 ANO2016 ANO2017 MES02 MES03 MES04 MES05 MES06 MES07
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##           MES08 MES09 MES10 MES11 MES12 DIA2 DIA3 DIA4 DIA5 DIA6 DIA7
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##           SEMANA02 SEMANA03 SEMANA04 SEMANA05 SEMANA06 SEMANA07 SEMANA08
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##           SEMANA09 SEMANA10 SEMANA11 SEMANA12 SEMANA13 SEMANA14 SEMANA15
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##           SEMANA16 SEMANA17 SEMANA18 SEMANA19 SEMANA20 SEMANA21 SEMANA22
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##           SEMANA23 SEMANA24 SEMANA25 SEMANA26 SEMANA27 SEMANA28 SEMANA29
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##           SEMANA30 SEMANA31 SEMANA32 SEMANA33 SEMANA34 SEMANA35 SEMANA36
## 1  ( 1 )  " "      " "      " "      " "      " "      " "      " "     
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##           SEMANA37 SEMANA38 SEMANA39 SEMANA40 SEMANA41 SEMANA42 SEMANA43
## 1  ( 1 )  " "      " "      " "      " "      " "      " "      " "     
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##           SEMANA44 SEMANA45 SEMANA46 SEMANA47 SEMANA48 SEMANA49 SEMANA50
## 1  ( 1 )  " "      " "      " "      " "      " "      " "      " "     
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##           SEMANA51 SEMANA52 SEMANA53 Feriado_Lunes Feriado_Otro
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##           Previo_feriado Prima Mujer Padre Madre AmoryAmistad Semana_Santa
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##           Viernes_Antes_Puente Quincena Viernes_Desp_Quincena_v1
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##           Viernes_Desp_Quincena_v2 Feria_Flores
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reg.summary =summary(regfit.fwd)
names(reg.summary)
## [1] "which"  "rsq"    "rss"    "adjr2"  "cp"     "bic"    "outmat" "obj"
reg.summary$rsq
##  [1] 0.3619356 0.4868724 0.5461869 0.5647899 0.5811803 0.5914461 0.6001406
##  [8] 0.6072893 0.6127937 0.6168722 0.6205206 0.6232782 0.6257787 0.6276244
## [15] 0.6291780 0.6305914 0.6319706 0.6332380 0.6345182 0.6357928 0.6371520
## [22] 0.6384302 0.6395528 0.6407111 0.6417461 0.6428253 0.6438054 0.6447313
## [29] 0.6458419 0.6470126 0.6481664 0.6501191 0.6575072 0.6658274 0.6673136
## [36] 0.6680038 0.6694037 0.6700405 0.6707762 0.6727615 0.6733687 0.6742044
## [43] 0.6747126 0.6752040 0.6756863 0.6761555 0.6765963 0.6770235 0.6773791
## [50] 0.6777361 0.6780563 0.6784142 0.6787212 0.6789833 0.6792370 0.6795436
## [57] 0.6797373 0.6799206 0.6802358 0.6804313 0.6805933 0.6807395 0.6808583
## [64] 0.6812809 0.6814133 0.6815839 0.6817376 0.6819732 0.6821051 0.6821782
## [71] 0.6822508 0.6823226 0.6823917 0.6824408 0.6827324 0.6829369 0.6834268
## [78] 0.6836086 0.6846287 0.6847462 0.6866739

Selección de variables con el mejor R^2 ajustado

max_adjr<-which.max (reg.summary$adjr2)
max_adjr
## [1] 81
par(mfrow =c(2,2))
plot(reg.summary$rss ,xlab=" Number of Variables ",ylab=" RSS",
type="l")
plot(reg.summary$adjr2 ,xlab =" Number of Variables ",
ylab=" Adjusted RSq",type="l")
points (max_adjr, reg.summary$adjr2[max_adjr], col ="red",cex =2, pch =20)

plot(regfit.fwd ,scale ="adjr2")

coef(regfit.fwd ,max_adjr)
##              (Intercept)                  ANO2015                  ANO2016 
##               91.5331404                1.4597935                3.3094593 
##                  ANO2017                    MES02                    MES03 
##                2.8862101               10.1318470                8.3480093 
##                    MES04                    MES05                    MES06 
##               -1.8588087                0.8584357               -4.4074025 
##                    MES07                    MES08                    MES09 
##               -6.7741762                0.5131129                5.7563074 
##                    MES10                    MES11                     DIA3 
##                7.5011095                5.8601290               -3.0082054 
##                     DIA4                     DIA5                     DIA6 
##               -2.8401209                1.9100357              -10.7965815 
##                     DIA7                 SEMANA02                 SEMANA03 
##              -49.2320369                9.9391395               25.2484541 
##                 SEMANA04                 SEMANA05                 SEMANA06 
##               25.7372255               24.8610857               25.0235625 
##                 SEMANA07                 SEMANA08                 SEMANA09 
##               27.5602585               24.3782785               24.5568933 
##                 SEMANA10                 SEMANA11                 SEMANA12 
##               32.0312685               29.9689863               21.1349492 
##                 SEMANA13                 SEMANA14                 SEMANA15 
##               28.4389418               34.7949816               34.9648516 
##                 SEMANA16                 SEMANA17                 SEMANA18 
##               27.5404099               42.8269575               39.0298950 
##                 SEMANA19                 SEMANA20                 SEMANA21 
##               35.3718898               36.7380164               35.6442715 
##                 SEMANA22                 SEMANA23                 SEMANA24 
##               32.6505866               42.3392837               37.5205753 
##                 SEMANA25                 SEMANA26                 SEMANA27 
##               32.0955776               26.5140433               38.8450921 
##                 SEMANA28                 SEMANA29                 SEMANA30 
##               40.8407908               44.7709308               44.0765940 
##                 SEMANA31                 SEMANA32                 SEMANA33 
##               45.7860746               41.0110605               38.5148798 
##                 SEMANA34                 SEMANA35                 SEMANA36 
##               33.4166622               36.5006637               29.4490355 
##                 SEMANA37                 SEMANA38                 SEMANA39 
##               36.3051582               36.3051582               27.4090784 
##                 SEMANA40                 SEMANA41                 SEMANA42 
##               34.3032214               19.3534264               28.2717101 
##                 SEMANA43                 SEMANA44                 SEMANA45 
##               27.8328714               27.0802680               23.9883723 
##                 SEMANA46                 SEMANA47                 SEMANA48 
##               30.0088245               28.5590973               28.9610670 
##                 SEMANA49                 SEMANA50                 SEMANA51 
##               35.1462102               36.5252278               41.5081224 
##                 SEMANA52                 SEMANA53            Feriado_Lunes 
##               20.0776865                1.7627823              -55.2274120 
##             Feriado_Otro                    Mujer                    Padre 
##              -46.1437891                7.1829221               10.7271739 
##                    Madre             AmoryAmistad     Viernes_Antes_Puente 
##               16.1402040                1.5381099                3.2515389 
##                 Quincena Viernes_Desp_Quincena_v1             Feria_Flores 
##               -1.2908487                2.0679537                6.5670045 
##                    Prima 
##                0.8736456

4. Modelamiento

set.seed(123) # fija la semilla del generador de parámetros para que sea reproducible

Se realiza el modelo de regresión lineal con las variables seleccionadas y se revisa el p-valor de cada una para seleccionar las variables definitivas del modelo

library(caret)
## Loading required package: lattice
## 
## Attaching package: 'caret'
## The following object is masked from 'package:purrr':
## 
##     lift
trcntrl = trainControl(method="cv", number=10)
caret_lm_fit = caret::train(TOTAL_ACCIDENTES∼ANO+MES+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Previo_feriado+Mujer+Padre+Madre+AmoryAmistad+Semana_Santa+Viernes_Antes_Puente+Quincena+Viernes_Desp_Quincena_v1+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
                      method = "lm", trControl = trcntrl,
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
summary(caret_lm_fit)
## 
## Call:
## lm(formula = .outcome ~ ., data = dat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -59.655  -9.817  -0.301   8.364  60.901 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              115.72758    0.39372 293.937  < 2e-16 ***
## ANO2015                    0.63790    0.48369   1.319 0.187447    
## ANO2016                    1.31357    0.49048   2.678 0.007492 ** 
## ANO2017                    1.15567    0.49042   2.356 0.018589 *  
## MES02                      2.81047    1.46850   1.914 0.055849 .  
## MES03                      2.49379    2.07802   1.200 0.230314    
## MES04                     -1.36187    2.40636  -0.566 0.571523    
## MES05                     -0.68034    2.69979  -0.252 0.801082    
## MES06                     -1.73599    2.79289  -0.622 0.534325    
## MES07                     -2.43242    2.89557  -0.840 0.401027    
## MES08                     -0.15743    2.86236  -0.055 0.956146    
## MES09                      1.36931    2.69708   0.508 0.611745    
## MES10                      2.11948    2.54224   0.834 0.404591    
## MES11                      1.76505    2.21645   0.796 0.425970    
## MES12                      0.47388    1.74247   0.272 0.785697    
## DIA2                      -0.14108    0.55304  -0.255 0.798680    
## DIA3                      -1.13362    0.55359  -2.048 0.040773 *  
## DIA4                      -1.14256    0.55587  -2.055 0.040025 *  
## DIA5                       0.56261    0.66007   0.852 0.394163    
## DIA6                      -3.65859    0.57668  -6.344 3.03e-10 ***
## DIA7                     -17.16045    0.57897 -29.639  < 2e-16 ***
## SEMANA02                   1.39394    0.57673   2.417 0.015779 *  
## SEMANA03                   3.50892    0.57956   6.054 1.81e-09 ***
## SEMANA04                   3.56673    0.57827   6.168 9.08e-10 ***
## SEMANA05                   3.41426    0.69869   4.887 1.15e-06 ***
## SEMANA06                   3.41765    0.93976   3.637 0.000286 ***
## SEMANA07                   3.73395    0.94062   3.970 7.57e-05 ***
## SEMANA08                   3.34095    0.94026   3.553 0.000393 ***
## SEMANA09                   3.31931    0.98631   3.365 0.000785 ***
## SEMANA10                   4.35198    1.16860   3.724 0.000204 ***
## SEMANA11                   4.24921    1.15801   3.669 0.000252 ***
## SEMANA12                   3.52306    1.16096   3.035 0.002454 ** 
## SEMANA13                   4.11533    1.14986   3.579 0.000357 ***
## SEMANA14                   6.03720    1.28650   4.693 2.96e-06 ***
## SEMANA15                   6.09122    1.32362   4.602 4.57e-06 ***
## SEMANA16                   4.90002    1.32069   3.710 0.000215 ***
## SEMANA17                   6.31946    1.30736   4.834 1.49e-06 ***
## SEMANA18                   5.80538    1.35362   4.289 1.92e-05 ***
## SEMANA19                   5.34226    1.42694   3.744 0.000189 ***
## SEMANA20                   5.53195    1.42696   3.877 0.000111 ***
## SEMANA21                   5.39685    1.42613   3.784 0.000161 ***
## SEMANA22                   4.92935    1.39804   3.526 0.000436 ***
## SEMANA23                   6.23588    1.48311   4.205 2.78e-05 ***
## SEMANA24                   5.55857    1.48855   3.734 0.000196 ***
## SEMANA25                   4.86318    1.48420   3.277 0.001077 ** 
## SEMANA26                   4.04286    1.46048   2.768 0.005713 ** 
## SEMANA27                   5.66544    1.49034   3.801 0.000150 ***
## SEMANA28                   5.89159    1.50894   3.904 9.90e-05 ***
## SEMANA29                   6.38997    1.50698   4.240 2.38e-05 ***
## SEMANA30                   6.37292    1.53198   4.160 3.38e-05 ***
## SEMANA31                   6.56533    1.47688   4.445 9.48e-06 ***
## SEMANA32                   5.82592    1.47639   3.946 8.35e-05 ***
## SEMANA33                   5.47489    1.48580   3.685 0.000238 ***
## SEMANA34                   4.76880    1.48956   3.201 0.001398 ** 
## SEMANA35                   5.15192    1.43139   3.599 0.000330 ***
## SEMANA36                   4.20665    1.41636   2.970 0.003029 ** 
## SEMANA37                   5.09974    1.42352   3.582 0.000352 ***
## SEMANA38                   5.14839    1.42368   3.616 0.000310 ***
## SEMANA39                   3.87478    1.39122   2.785 0.005423 ** 
## SEMANA40                   4.77100    1.30836   3.647 0.000276 ***
## SEMANA41                   2.71063    1.32334   2.048 0.040718 *  
## SEMANA42                   3.90902    1.32514   2.950 0.003233 ** 
## SEMANA43                   3.84189    1.32240   2.905 0.003728 ** 
## SEMANA44                   3.72198    1.19699   3.109 0.001913 ** 
## SEMANA45                   3.26752    1.18352   2.761 0.005842 ** 
## SEMANA46                   4.06205    1.18385   3.431 0.000619 ***
## SEMANA47                   3.88913    1.18593   3.279 0.001066 ** 
## SEMANA48                   3.89400    1.07740   3.614 0.000312 ***
## SEMANA49                   4.72884    0.94379   5.010 6.14e-07 ***
## SEMANA50                   4.88178    0.94594   5.161 2.82e-07 ***
## SEMANA51                   5.62281    0.94443   5.954 3.32e-09 ***
## SEMANA52                   2.61486    0.92316   2.833 0.004686 ** 
## SEMANA53                   0.03037    0.48919   0.062 0.950512    
## Feriado_Lunes             -9.43456    0.45770 -20.613  < 2e-16 ***
## Feriado_Otro              -5.54755    0.43090 -12.874  < 2e-16 ***
## Previo_feriado            -0.17094    0.56717  -0.301 0.763158    
## Mujer                      0.36942    0.42549   0.868 0.385419    
## Padre                      0.54438    0.41641   1.307 0.191323    
## Madre                      0.83691    0.41672   2.008 0.044806 *  
## AmoryAmistad               0.05610    0.41230   0.136 0.891790    
## Semana_Santa              -3.10987    0.47057  -6.609 5.53e-11 ***
## Viernes_Antes_Puente       0.57704    0.53443   1.080 0.280452    
## Quincena                  -0.23694    0.40341  -0.587 0.557066    
## Viernes_Desp_Quincena_v1  -0.07675    0.65663  -0.117 0.906969    
## Viernes_Desp_Quincena_v2   0.82050    0.59789   1.372 0.170185    
## Feria_Flores               0.94102    0.67190   1.401 0.161578    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.05 on 1375 degrees of freedom
## Multiple R-squared:  0.6867, Adjusted R-squared:  0.6674 
## F-statistic: 35.46 on 85 and 1375 DF,  p-value: < 2.2e-16

1. REGRESION LINEAL

Total Accidentes

head(Train_D_Dataset)
##        FECHA ACCIDENTES_GRAVES ACCIDENTES_LEVES TOTAL_ACCIDENTES Ano_Base
## 1 2014-01-01                56               18               74        0
## 2 2014-01-02                42               30               72        0
## 3 2014-01-03                51               42               93        0
## 4 2014-01-04                41               27               68        0
## 5 2014-01-05                36               31               67        0
## 6 2014-01-06                29               14               43        0
##   Lunes martes miercoles jueves viernes sabado domingo Enero Febrero Marzo
## 1     0      0         1      0       0      0       0     1       0     0
## 2     0      0         0      1       0      0       0     1       0     0
## 3     0      0         0      0       1      0       0     1       0     0
## 4     0      0         0      0       0      1       0     1       0     0
## 5     0      0         0      0       0      0       1     1       0     0
## 6     1      0         0      0       0      0       0     1       0     0
##   Abril Mayo Junio Julio Agosto Septiembre Octubre Noviembre Diciembre
## 1     0    0     0     0      0          0       0         0         0
## 2     0    0     0     0      0          0       0         0         0
## 3     0    0     0     0      0          0       0         0         0
## 4     0    0     0     0      0          0       0         0         0
## 5     0    0     0     0      0          0       0         0         0
## 6     0    0     0     0      0          0       0         0         0
##   Feriado Feriado_v1 Feriado_Lunes Feriado_Otro Previo_feriado
## 1       1          1             0            1              0
## 2       0          0             0            0              0
## 3       0          0             0            0              1
## 4       0          0             0            0              1
## 5       0          0             0            0              1
## 6       1          1             1            0              0
##   Semana_Santa Semana_Santa_Mes Semana_Santa_Semana Prima Mujer Padre
## 1            0                0                   0     0     0     0
## 2            0                0                   0     0     0     0
## 3            0                0                   0     0     0     0
## 4            0                0                   0     0     0     0
## 5            0                0                   0     0     0     0
## 6            0                0                   0     0     0     0
##   Madre AmoryAmistad Semana_Santa_v1 Viernes_Antes_Puente Quincena
## 1     0            0               0                    0        0
## 2     0            0               0                    0        0
## 3     0            0               0                    1        0
## 4     0            0               0                    0        0
## 5     0            0               0                    0        0
## 6     0            0               0                    0        0
##   Viernes_Desp_Quincena Viernes_Desp_Quincena_v1 Viernes_Desp_Quincena_v2
## 1                     0                        0                        0
## 2                     0                        0                        0
## 3                     0                        0                        0
## 4                     0                        0                        0
## 5                     0                        0                        0
## 6                     0                        0                        0
##   Feria_Flores Feria_Flores_Mes Feria_Flores_Semana  ANO SEMANA MES DIA
## 1            0                0                   0 2014     01  01   3
## 2            0                0                   0 2014     01  01   4
## 3            0                0                   0 2014     01  01   5
## 4            0                0                   0 2014     01  01   6
## 5            0                0                   0 2014     01  01   7
## 6            0                0                   0 2014     02  01   1
library(caret)
trcntrl = trainControl(method="cv", number=10)
caret_lm_fit = caret::train(TOTAL_ACCIDENTES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
                      method = "lm", trControl = trcntrl,
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
summary(caret_lm_fit)
## 
## Call:
## lm(formula = .outcome ~ ., data = dat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -59.015  -9.875  -0.302   8.281  65.351 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              115.7276     0.3936 294.013  < 2e-16 ***
## Ano_Base                   1.0776     0.4011   2.686  0.00731 ** 
## DIA2                      -0.1253     0.5518  -0.227  0.82039    
## DIA3                      -1.1352     0.5524  -2.055  0.04006 *  
## DIA4                      -1.1460     0.5549  -2.065  0.03909 *  
## DIA5                       0.7339     0.5842   1.256  0.20927    
## DIA6                      -3.7236     0.5532  -6.731 2.45e-11 ***
## DIA7                     -17.1487     0.5562 -30.834  < 2e-16 ***
## SEMANA02                   1.3550     0.5644   2.401  0.01649 *  
## SEMANA03                   3.4624     0.5633   6.147 1.03e-09 ***
## SEMANA04                   3.5289     0.5630   6.268 4.86e-10 ***
## SEMANA05                   4.1389     0.5632   7.349 3.38e-13 ***
## SEMANA06                   4.8270     0.5630   8.574  < 2e-16 ***
## SEMANA07                   5.1260     0.5637   9.094  < 2e-16 ***
## SEMANA08                   4.7486     0.5630   8.435  < 2e-16 ***
## SEMANA09                   4.6019     0.5637   8.164 7.17e-16 ***
## SEMANA10                   5.6842     0.5630  10.096  < 2e-16 ***
## SEMANA11                   5.4337     0.5648   9.620  < 2e-16 ***
## SEMANA12                   4.7082     0.5720   8.231 4.21e-16 ***
## SEMANA13                   4.9672     0.5653   8.786  < 2e-16 ***
## SEMANA14                   5.5068     0.5777   9.533  < 2e-16 ***
## SEMANA15                   5.3599     0.5777   9.278  < 2e-16 ***
## SEMANA16                   4.1558     0.5716   7.271 5.94e-13 ***
## SEMANA17                   5.6184     0.5623   9.992  < 2e-16 ***
## SEMANA18                   5.3485     0.5637   9.488  < 2e-16 ***
## SEMANA19                   4.9672     0.5751   8.637  < 2e-16 ***
## SEMANA20                   5.1554     0.5637   9.146  < 2e-16 ***
## SEMANA21                   5.0419     0.5631   8.954  < 2e-16 ***
## SEMANA22                   4.3657     0.5642   7.738 1.93e-14 ***
## SEMANA23                   5.3402     0.5635   9.477  < 2e-16 ***
## SEMANA24                   4.8120     0.5638   8.535  < 2e-16 ***
## SEMANA25                   4.0177     0.5635   7.130 1.61e-12 ***
## SEMANA26                   3.1019     0.5642   5.498 4.56e-08 ***
## SEMANA27                   4.4525     0.5652   7.877 6.67e-15 ***
## SEMANA28                   4.6532     0.5632   8.262 3.28e-16 ***
## SEMANA29                   5.1612     0.5624   9.177  < 2e-16 ***
## SEMANA30                   5.0680     0.5736   8.836  < 2e-16 ***
## SEMANA31                   5.8182     0.6663   8.732  < 2e-16 ***
## SEMANA32                   5.5639     0.6149   9.048  < 2e-16 ***
## SEMANA33                   5.3655     0.5638   9.517  < 2e-16 ***
## SEMANA34                   4.6539     0.5642   8.248 3.67e-16 ***
## SEMANA35                   5.2191     0.5637   9.259  < 2e-16 ***
## SEMANA36                   4.8221     0.5630   8.565  < 2e-16 ***
## SEMANA37                   5.7530     0.5637  10.206  < 2e-16 ***
## SEMANA38                   5.7968     0.5630  10.296  < 2e-16 ***
## SEMANA39                   4.5552     0.5632   8.088 1.30e-15 ***
## SEMANA40                   5.7038     0.5630  10.131  < 2e-16 ***
## SEMANA41                   3.7396     0.5630   6.642 4.41e-11 ***
## SEMANA42                   4.9008     0.5654   8.668  < 2e-16 ***
## SEMANA43                   4.8515     0.5630   8.617  < 2e-16 ***
## SEMANA44                   4.6606     0.5637   8.268 3.13e-16 ***
## SEMANA45                   4.1243     0.5652   7.297 4.93e-13 ***
## SEMANA46                   4.9045     0.5642   8.693  < 2e-16 ***
## SEMANA47                   4.7328     0.5635   8.399  < 2e-16 ***
## SEMANA48                   4.5676     0.5637   8.103 1.16e-15 ***
## SEMANA49                   4.9550     0.5621   8.816  < 2e-16 ***
## SEMANA50                   5.0744     0.5631   9.011  < 2e-16 ***
## SEMANA51                   5.8192     0.5623  10.349  < 2e-16 ***
## SEMANA52                   2.8069     0.5623   4.992 6.74e-07 ***
## SEMANA53                   0.1113     0.4437   0.251  0.80197    
## Feriado_Lunes             -9.3842     0.4537 -20.684  < 2e-16 ***
## Feriado_Otro              -5.6005     0.4222 -13.264  < 2e-16 ***
## Madre                      0.8407     0.4158   2.022  0.04339 *  
## Semana_Santa              -3.0095     0.4672  -6.442 1.62e-10 ***
## Viernes_Desp_Quincena_v2   0.7397     0.4492   1.646  0.09989 .  
## Feria_Flores               1.3243     0.5918   2.238  0.02539 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.05 on 1395 degrees of freedom
## Multiple R-squared:  0.6823, Adjusted R-squared:  0.6675 
## F-statistic:  46.1 on 65 and 1395 DF,  p-value: < 2.2e-16
caret_lm_fit
## Linear Regression 
## 
## 1461 samples
##    9 predictor
## 
## Pre-processing: centered (65), scaled (65) 
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1315, 1315, 1315, 1314, 1315, 1314, ... 
## Resampling results:
## 
##   RMSE      Rsquared   MAE     
##   15.34565  0.6537082  11.93395
## 
## Tuning parameter 'intercept' was held constant at a value of TRUE

Calculo MSE y RMSE para los datos de entrenamiento

Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores

y_tr_pred_lm<-predict(caret_lm_fit,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_lm<-mean((Train_D_Dataset$TOTAL_ACCIDENTES-y_tr_pred_lm)^2) # calcula el mse de entrenamiento
RMSE_tr_lm = sqrt(mse_tr_lm)
mse_tr_lm
## [1] 216.1297
RMSE_tr_lm
## [1] 14.70135

Calculo MSE y RMSE para los datos de validación

y_test_pred_lm<-predict(caret_lm_fit,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_lm<-mean((Test_D_Dataset$TOTAL_ACCIDENTES-y_test_pred_lm)^2) # calcula el mse de entrenamiento
RMSE_test_lm = sqrt(mse_test_lm)
mse_test_lm
## [1] 256.8317
RMSE_test_lm
## [1] 16.02597

Predicción en la muestra

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:reshape':
## 
##     rename
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~TOTAL_ACCIDENTES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_lm,
            name='Modelo lm',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~TOTAL_ACCIDENTES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_lm,
            name='Modelo lm',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Accidentes Graves

trcntrl = trainControl(method="cv", number=10)
caret_lm_fit_m = caret::train(ACCIDENTES_GRAVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
                      method = "lm", trControl = trcntrl,
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
summary(caret_lm_fit_m)
## 
## Call:
## lm(formula = .outcome ~ ., data = dat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.636  -6.537  -0.422   6.278  47.122 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               64.4606     0.2628 245.282  < 2e-16 ***
## Ano_Base                   0.2202     0.2678   0.822 0.411017    
## DIA2                      -0.4368     0.3684  -1.186 0.235968    
## DIA3                      -0.4399     0.3688  -1.193 0.233148    
## DIA4                      -0.2523     0.3705  -0.681 0.496022    
## DIA5                      -0.4151     0.3901  -1.064 0.287403    
## DIA6                      -1.3858     0.3694  -3.752 0.000183 ***
## DIA7                      -6.2262     0.3713 -16.767  < 2e-16 ***
## SEMANA02                   0.2058     0.3768   0.546 0.585120    
## SEMANA03                   1.5883     0.3761   4.223 2.57e-05 ***
## SEMANA04                   1.2446     0.3759   3.311 0.000953 ***
## SEMANA05                   1.9048     0.3760   5.066 4.61e-07 ***
## SEMANA06                   2.4447     0.3759   6.504 1.09e-10 ***
## SEMANA07                   2.0164     0.3763   5.358 9.85e-08 ***
## SEMANA08                   2.2830     0.3759   6.074 1.61e-09 ***
## SEMANA09                   2.0311     0.3763   5.397 7.96e-08 ***
## SEMANA10                   2.6406     0.3759   7.025 3.34e-12 ***
## SEMANA11                   2.6676     0.3771   7.074 2.38e-12 ***
## SEMANA12                   2.1047     0.3819   5.511 4.24e-08 ***
## SEMANA13                   2.3333     0.3775   6.182 8.31e-10 ***
## SEMANA14                   2.4926     0.3857   6.463 1.42e-10 ***
## SEMANA15                   2.1595     0.3857   5.599 2.59e-08 ***
## SEMANA16                   1.9295     0.3816   5.056 4.85e-07 ***
## SEMANA17                   2.3504     0.3754   6.260 5.10e-10 ***
## SEMANA18                   2.1095     0.3764   5.605 2.51e-08 ***
## SEMANA19                   2.1563     0.3840   5.616 2.36e-08 ***
## SEMANA20                   2.1927     0.3763   5.826 7.03e-09 ***
## SEMANA21                   2.2506     0.3760   5.986 2.73e-09 ***
## SEMANA22                   1.9270     0.3767   5.116 3.56e-07 ***
## SEMANA23                   2.5660     0.3762   6.820 1.35e-11 ***
## SEMANA24                   2.1456     0.3764   5.700 1.46e-08 ***
## SEMANA25                   1.7088     0.3762   4.542 6.06e-06 ***
## SEMANA26                   1.0453     0.3767   2.775 0.005593 ** 
## SEMANA27                   1.9672     0.3774   5.213 2.14e-07 ***
## SEMANA28                   2.1154     0.3760   5.626 2.23e-08 ***
## SEMANA29                   2.2439     0.3755   5.976 2.90e-09 ***
## SEMANA30                   2.2346     0.3829   5.835 6.67e-09 ***
## SEMANA31                   2.7308     0.4449   6.139 1.08e-09 ***
## SEMANA32                   2.3822     0.4106   5.802 8.08e-09 ***
## SEMANA33                   2.4542     0.3764   6.520 9.81e-11 ***
## SEMANA34                   2.2249     0.3767   5.906 4.39e-09 ***
## SEMANA35                   2.8197     0.3763   7.492 1.20e-13 ***
## SEMANA36                   2.0920     0.3759   5.565 3.13e-08 ***
## SEMANA37                   2.7217     0.3763   7.232 7.82e-13 ***
## SEMANA38                   2.6210     0.3759   6.973 4.78e-12 ***
## SEMANA39                   2.2134     0.3760   5.886 4.94e-09 ***
## SEMANA40                   2.4496     0.3759   6.517 1.00e-10 ***
## SEMANA41                   1.2936     0.3759   3.441 0.000596 ***
## SEMANA42                   2.1767     0.3775   5.766 9.97e-09 ***
## SEMANA43                   2.0969     0.3759   5.579 2.91e-08 ***
## SEMANA44                   1.7862     0.3763   4.746 2.29e-06 ***
## SEMANA45                   1.7223     0.3774   4.564 5.47e-06 ***
## SEMANA46                   1.7850     0.3767   4.739 2.37e-06 ***
## SEMANA47                   1.8165     0.3762   4.828 1.53e-06 ***
## SEMANA48                   1.6098     0.3763   4.278 2.02e-05 ***
## SEMANA49                   1.5801     0.3753   4.211 2.71e-05 ***
## SEMANA50                   1.6692     0.3760   4.440 9.72e-06 ***
## SEMANA51                   2.1104     0.3754   5.621 2.29e-08 ***
## SEMANA52                   1.1986     0.3754   3.193 0.001441 ** 
## SEMANA53                  -0.2564     0.2963  -0.865 0.386997    
## Feriado_Lunes             -3.4841     0.3029 -11.502  < 2e-16 ***
## Feriado_Otro              -1.9867     0.2819  -7.047 2.86e-12 ***
## Madre                      0.3182     0.2776   1.146 0.251983    
## Semana_Santa              -1.6247     0.3119  -5.208 2.19e-07 ***
## Viernes_Desp_Quincena_v2   0.5320     0.2999   1.774 0.076341 .  
## Feria_Flores               0.4970     0.3951   1.258 0.208673    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.05 on 1395 degrees of freedom
## Multiple R-squared:  0.4236, Adjusted R-squared:  0.3967 
## F-statistic: 15.77 on 65 and 1395 DF,  p-value: < 2.2e-16
caret_lm_fit_m
## Linear Regression 
## 
## 1461 samples
##    9 predictor
## 
## Pre-processing: centered (65), scaled (65) 
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1314, 1315, 1315, 1316, 1317, 1315, ... 
## Resampling results:
## 
##   RMSE      Rsquared   MAE     
##   10.28203  0.3716403  8.066983
## 
## Tuning parameter 'intercept' was held constant at a value of TRUE

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_lm_m<-predict(caret_lm_fit_m,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_lm_m<-mean((Train_D_Dataset$ACCIDENTES_GRAVES-y_tr_pred_lm_m)^2) # calcula el mse de entrenamiento
RMSE_tr_lm_m = sqrt(mse_tr_lm_m)
mse_tr_lm_m
## [1] 96.34588
RMSE_tr_lm_m
## [1] 9.815594

Calculo MSE y RMSE para los datos de validación

y_test_pred_lm_m<-predict(caret_lm_fit_m,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_lm_m<-mean((Test_D_Dataset$ACCIDENTES_GRAVES-y_test_pred_lm_m)^2) # calcula el mse de entrenamiento
RMSE_test_lm_m = sqrt(mse_test_lm_m)
mse_test_lm_m
## [1] 128.5295
RMSE_test_lm_m
## [1] 11.33709

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_GRAVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_lm_m,
            name='Modelo lm',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes graves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes graves"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_GRAVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_lm_m,
            name='Modelo lm',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes graves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes graves"),
         legend = list(x = 0.75, y = 0.9))

Accidentes Leves

trcntrl = trainControl(method="cv", number=10)
caret_lm_fit_sd = caret::train(ACCIDENTES_LEVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
                      method = "lm", trControl = trcntrl,
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
summary(caret_lm_fit_sd)
## 
## Call:
## lm(formula = .outcome ~ ., data = dat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.441  -5.720  -0.491   5.764  39.811 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               51.2669     0.2473 207.305  < 2e-16 ***
## Ano_Base                   0.8573     0.2520   3.402 0.000689 ***
## DIA2                       0.3115     0.3467   0.899 0.369072    
## DIA3                      -0.6953     0.3471  -2.003 0.045344 *  
## DIA4                      -0.8937     0.3486  -2.563 0.010471 *  
## DIA5                       1.1490     0.3671   3.130 0.001783 ** 
## DIA6                      -2.3378     0.3476  -6.726 2.53e-11 ***
## DIA7                     -10.9224     0.3494 -31.258  < 2e-16 ***
## SEMANA02                   1.1493     0.3546   3.241 0.001219 ** 
## SEMANA03                   1.8741     0.3539   5.296 1.38e-07 ***
## SEMANA04                   2.2843     0.3537   6.458 1.46e-10 ***
## SEMANA05                   2.2341     0.3538   6.314 3.65e-10 ***
## SEMANA06                   2.3823     0.3537   6.735 2.39e-11 ***
## SEMANA07                   3.1096     0.3541   8.780  < 2e-16 ***
## SEMANA08                   2.4656     0.3537   6.970 4.86e-12 ***
## SEMANA09                   2.5708     0.3541   7.259 6.45e-13 ***
## SEMANA10                   3.0435     0.3537   8.604  < 2e-16 ***
## SEMANA11                   2.7660     0.3549   7.794 1.26e-14 ***
## SEMANA12                   2.6036     0.3594   7.245 7.15e-13 ***
## SEMANA13                   2.6339     0.3552   7.415 2.10e-13 ***
## SEMANA14                   3.0142     0.3629   8.305 2.34e-16 ***
## SEMANA15                   3.2004     0.3629   8.818  < 2e-16 ***
## SEMANA16                   2.2263     0.3591   6.199 7.45e-10 ***
## SEMANA17                   3.2680     0.3533   9.250  < 2e-16 ***
## SEMANA18                   3.2390     0.3542   9.145  < 2e-16 ***
## SEMANA19                   2.8110     0.3613   7.780 1.41e-14 ***
## SEMANA20                   2.9627     0.3541   8.366  < 2e-16 ***
## SEMANA21                   2.7913     0.3538   7.890 6.07e-15 ***
## SEMANA22                   2.4386     0.3545   6.880 9.03e-12 ***
## SEMANA23                   2.7742     0.3540   7.836 9.17e-15 ***
## SEMANA24                   2.6664     0.3542   7.527 9.24e-14 ***
## SEMANA25                   2.3089     0.3540   6.522 9.71e-11 ***
## SEMANA26                   2.0566     0.3545   5.802 8.11e-09 ***
## SEMANA27                   2.4854     0.3551   6.999 4.00e-12 ***
## SEMANA28                   2.5378     0.3538   7.172 1.19e-12 ***
## SEMANA29                   2.9172     0.3534   8.256 3.46e-16 ***
## SEMANA30                   2.8334     0.3604   7.863 7.47e-15 ***
## SEMANA31                   3.0873     0.4186   7.375 2.81e-13 ***
## SEMANA32                   3.1817     0.3863   8.235 4.07e-16 ***
## SEMANA33                   2.9113     0.3542   8.219 4.64e-16 ***
## SEMANA34                   2.4290     0.3545   6.852 1.09e-11 ***
## SEMANA35                   2.3994     0.3541   6.775 1.83e-11 ***
## SEMANA36                   2.7301     0.3537   7.718 2.24e-14 ***
## SEMANA37                   3.0312     0.3541   8.559  < 2e-16 ***
## SEMANA38                   3.1758     0.3537   8.978  < 2e-16 ***
## SEMANA39                   2.3418     0.3538   6.618 5.16e-11 ***
## SEMANA40                   3.2542     0.3537   9.200  < 2e-16 ***
## SEMANA41                   2.4460     0.3537   6.915 7.10e-12 ***
## SEMANA42                   2.7241     0.3552   7.668 3.25e-14 ***
## SEMANA43                   2.7546     0.3537   7.787 1.33e-14 ***
## SEMANA44                   2.8745     0.3541   8.117 1.04e-15 ***
## SEMANA45                   2.4021     0.3551   6.764 1.97e-11 ***
## SEMANA46                   3.1195     0.3545   8.800  < 2e-16 ***
## SEMANA47                   2.9163     0.3540   8.237 4.02e-16 ***
## SEMANA48                   2.9578     0.3541   8.352  < 2e-16 ***
## SEMANA49                   3.3749     0.3531   9.557  < 2e-16 ***
## SEMANA50                   3.4051     0.3538   9.625  < 2e-16 ***
## SEMANA51                   3.7088     0.3533  10.498  < 2e-16 ***
## SEMANA52                   1.6083     0.3533   4.552 5.77e-06 ***
## SEMANA53                   0.3677     0.2788   1.319 0.187439    
## Feriado_Lunes             -5.9001     0.2851 -20.698  < 2e-16 ***
## Feriado_Otro              -3.6138     0.2653 -13.622  < 2e-16 ***
## Madre                      0.5225     0.2613   2.000 0.045686 *  
## Semana_Santa              -1.3848     0.2935  -4.718 2.62e-06 ***
## Viernes_Desp_Quincena_v2   0.2077     0.2822   0.736 0.461970    
## Feria_Flores               0.8274     0.3718   2.225 0.026224 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.453 on 1395 degrees of freedom
## Multiple R-squared:  0.6824, Adjusted R-squared:  0.6676 
## F-statistic: 46.11 on 65 and 1395 DF,  p-value: < 2.2e-16
caret_lm_fit_sd
## Linear Regression 
## 
## 1461 samples
##    9 predictor
## 
## Pre-processing: centered (65), scaled (65) 
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1315, 1315, 1316, 1315, 1315, 1316, ... 
## Resampling results:
## 
##   RMSE      Rsquared  MAE     
##   9.740971  0.649214  7.557774
## 
## Tuning parameter 'intercept' was held constant at a value of TRUE

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_lm_sd<-predict(caret_lm_fit_sd,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_lm_sd<-mean((Train_D_Dataset$ACCIDENTES_LEVES-y_tr_pred_lm_sd)^2) # calcula el mse de entrenamiento
RMSE_tr_lm_sd = sqrt(mse_tr_lm_sd)
mse_tr_lm_sd
## [1] 85.31586
RMSE_tr_lm_sd
## [1] 9.236658

Calculo MSE y RMSE para los datos de validación

y_test_pred_lm_sd<-predict(caret_lm_fit_sd,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_lm_sd<-mean((Test_D_Dataset$ACCIDENTES_LEVES-y_test_pred_lm_sd)^2) # calcula el mse de entrenamiento
RMSE_test_lm_sd = sqrt(mse_test_lm_sd)
mse_test_lm_sd
## [1] 108.411
RMSE_test_lm_sd
## [1] 10.41206

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_LEVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_lm_sd,
            name='Modelo lm',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes leves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes leves"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_LEVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_lm_sd,
            name='Modelo lm',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes leves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes leves"),
         legend = list(x = 0.75, y = 0.9))

Resumen Modelos Regresión lineal para los diferentes tipos de accidente

Tipo_de_accidentes= c("Total Accidentes","Accidentes graves","Accidentes leves")
RMSE_Train_lm = round(c(RMSE_tr_lm,RMSE_tr_lm_m,RMSE_tr_lm_sd), 3)
RMSE_Test_lm = round(c(RMSE_test_lm,RMSE_test_lm_m,RMSE_test_lm_sd),3)

Tabla_lm = data.frame (cbind(Tipo_de_accidentes,RMSE_Train_lm,RMSE_Test_lm))
Tabla_lm
##   Tipo_de_accidentes RMSE_Train_lm RMSE_Test_lm
## 1   Total Accidentes        14.701       16.026
## 2  Accidentes graves         9.816       11.337
## 3   Accidentes leves         9.237       10.412

2. KNN

head(Train_D_Dataset)
##        FECHA ACCIDENTES_GRAVES ACCIDENTES_LEVES TOTAL_ACCIDENTES Ano_Base
## 1 2014-01-01                56               18               74        0
## 2 2014-01-02                42               30               72        0
## 3 2014-01-03                51               42               93        0
## 4 2014-01-04                41               27               68        0
## 5 2014-01-05                36               31               67        0
## 6 2014-01-06                29               14               43        0
##   Lunes martes miercoles jueves viernes sabado domingo Enero Febrero Marzo
## 1     0      0         1      0       0      0       0     1       0     0
## 2     0      0         0      1       0      0       0     1       0     0
## 3     0      0         0      0       1      0       0     1       0     0
## 4     0      0         0      0       0      1       0     1       0     0
## 5     0      0         0      0       0      0       1     1       0     0
## 6     1      0         0      0       0      0       0     1       0     0
##   Abril Mayo Junio Julio Agosto Septiembre Octubre Noviembre Diciembre
## 1     0    0     0     0      0          0       0         0         0
## 2     0    0     0     0      0          0       0         0         0
## 3     0    0     0     0      0          0       0         0         0
## 4     0    0     0     0      0          0       0         0         0
## 5     0    0     0     0      0          0       0         0         0
## 6     0    0     0     0      0          0       0         0         0
##   Feriado Feriado_v1 Feriado_Lunes Feriado_Otro Previo_feriado
## 1       1          1             0            1              0
## 2       0          0             0            0              0
## 3       0          0             0            0              1
## 4       0          0             0            0              1
## 5       0          0             0            0              1
## 6       1          1             1            0              0
##   Semana_Santa Semana_Santa_Mes Semana_Santa_Semana Prima Mujer Padre
## 1            0                0                   0     0     0     0
## 2            0                0                   0     0     0     0
## 3            0                0                   0     0     0     0
## 4            0                0                   0     0     0     0
## 5            0                0                   0     0     0     0
## 6            0                0                   0     0     0     0
##   Madre AmoryAmistad Semana_Santa_v1 Viernes_Antes_Puente Quincena
## 1     0            0               0                    0        0
## 2     0            0               0                    0        0
## 3     0            0               0                    1        0
## 4     0            0               0                    0        0
## 5     0            0               0                    0        0
## 6     0            0               0                    0        0
##   Viernes_Desp_Quincena Viernes_Desp_Quincena_v1 Viernes_Desp_Quincena_v2
## 1                     0                        0                        0
## 2                     0                        0                        0
## 3                     0                        0                        0
## 4                     0                        0                        0
## 5                     0                        0                        0
## 6                     0                        0                        0
##   Feria_Flores Feria_Flores_Mes Feria_Flores_Semana  ANO SEMANA MES DIA
## 1            0                0                   0 2014     01  01   3
## 2            0                0                   0 2014     01  01   4
## 3            0                0                   0 2014     01  01   5
## 4            0                0                   0 2014     01  01   6
## 5            0                0                   0 2014     01  01   7
## 6            0                0                   0 2014     02  01   1

Total Accidentes

library(caret)
trcntrl = trainControl(method="cv", number=10)
caret_knn_fit = caret::train(TOTAL_ACCIDENTES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
                      method = "knn", trControl = trcntrl,
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
summary(caret_knn_fit)
##             Length Class      Mode     
## learn        2     -none-     list     
## k            1     -none-     numeric  
## theDots      0     -none-     list     
## xNames      65     -none-     character
## problemType  1     -none-     character
## tuneValue    1     data.frame list     
## obsLevels    1     -none-     logical  
## param        0     -none-     list
caret_knn_fit
## k-Nearest Neighbors 
## 
## 1461 samples
##    9 predictor
## 
## Pre-processing: centered (65), scaled (65) 
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1315, 1315, 1315, 1316, 1315, 1315, ... 
## Resampling results across tuning parameters:
## 
##   k   RMSE      Rsquared   MAE     
##    5  21.10115  0.3824778  16.62695
##    7  22.19154  0.3152980  17.49614
##    9  23.03506  0.2534354  18.00843
##   11  23.49203  0.2133221  18.25785
##   13  23.77937  0.1872929  18.44687
##   15  24.07922  0.1625583  18.69741
##   17  24.48434  0.1315898  18.96446
##   19  24.62593  0.1187803  19.10631
##   21  24.83848  0.1020593  19.37665
##   23  24.76398  0.1031986  19.49218
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 5.

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_knn<-predict(caret_knn_fit,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_knn<-mean((Train_D_Dataset$TOTAL_ACCIDENTES-y_tr_pred_knn)^2) # calcula el mse de entrenamiento
RMSE_tr_knn = sqrt(mse_tr_knn)
mse_tr_knn
## [1] 305.9636
RMSE_tr_knn
## [1] 17.49181

Calculo MSE y RMSE para los datos de validación

y_test_pred_knn<-predict(caret_knn_fit,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_knn<-mean((Test_D_Dataset$TOTAL_ACCIDENTES-y_test_pred_knn)^2) # calcula el mse de entrenamiento
RMSE_test_knn = sqrt(mse_test_knn)
mse_test_knn
## [1] 437.2166
RMSE_test_knn
## [1] 20.90972

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~TOTAL_ACCIDENTES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_knn,
            name='Modelo knn',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total Accidentes',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~TOTAL_ACCIDENTES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_knn,
            name='Modelo knn',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total Accidentes',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Accidentes graves

trcntrl = trainControl(method="cv", number=10)
caret_knn_fit_m = caret::train(ACCIDENTES_GRAVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
                      method = "knn", trControl = trcntrl,
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
summary(caret_knn_fit_m)
##             Length Class      Mode     
## learn        2     -none-     list     
## k            1     -none-     numeric  
## theDots      0     -none-     list     
## xNames      65     -none-     character
## problemType  1     -none-     character
## tuneValue    1     data.frame list     
## obsLevels    1     -none-     logical  
## param        0     -none-     list
caret_knn_fit_m
## k-Nearest Neighbors 
## 
## 1461 samples
##    9 predictor
## 
## Pre-processing: centered (65), scaled (65) 
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1314, 1314, 1315, 1315, 1316, 1315, ... 
## Resampling results across tuning parameters:
## 
##   k   RMSE      Rsquared    MAE     
##    5  11.63945  0.21901439  9.233752
##    7  11.81686  0.18953717  9.335533
##    9  11.96100  0.16399084  9.379428
##   11  11.96903  0.15666172  9.363416
##   13  12.02683  0.14625821  9.366623
##   15  12.10142  0.13512843  9.399686
##   17  12.17010  0.12553370  9.470361
##   19  12.24001  0.11374812  9.511199
##   21  12.28834  0.10543240  9.571437
##   23  12.31755  0.09927642  9.615764
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 5.

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_knn_m<-predict(caret_knn_fit_m,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_knn_m<-mean((Train_D_Dataset$ACCIDENTES_GRAVES-y_tr_pred_knn_m)^2) # calcula el mse de entrenamiento
RMSE_tr_knn_m = sqrt(mse_tr_knn_m)
mse_tr_knn_m
## [1] 96.31957
RMSE_tr_knn_m
## [1] 9.814253

Calculo MSE y RMSE para los datos de validación

y_test_pred_knn_m<-predict(caret_knn_fit_m,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_knn_m<-mean((Test_D_Dataset$ACCIDENTES_GRAVES-y_test_pred_knn_m)^2) # calcula el mse de entrenamiento
RMSE_test_knn_m = sqrt(mse_test_knn_m)
mse_test_knn_m
## [1] 179.9912
RMSE_test_knn_m
## [1] 13.41608

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_GRAVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_knn_m,
            name='Modelo knn',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes graves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes graves"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_GRAVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_knn_m,
            name='Modelo knn',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes graves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes graves"),
         legend = list(x = 0.75, y = 0.9))

Accidentes Leves

trcntrl = trainControl(method="cv", number=10)
caret_knn_fit_sd = caret::train(ACCIDENTES_LEVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
                      method = "knn", trControl = trcntrl,
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
summary(caret_knn_fit_sd)
##             Length Class      Mode     
## learn        2     -none-     list     
## k            1     -none-     numeric  
## theDots      0     -none-     list     
## xNames      65     -none-     character
## problemType  1     -none-     character
## tuneValue    1     data.frame list     
## obsLevels    1     -none-     logical  
## param        0     -none-     list
caret_knn_fit_sd
## k-Nearest Neighbors 
## 
## 1461 samples
##    9 predictor
## 
## Pre-processing: centered (65), scaled (65) 
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1316, 1313, 1315, 1316, 1316, 1314, ... 
## Resampling results across tuning parameters:
## 
##   k   RMSE      Rsquared    MAE     
##    5  13.46482  0.36078923  10.78935
##    7  14.18933  0.28497651  11.34931
##    9  14.74803  0.21045008  11.73284
##   11  15.06144  0.16669188  11.93018
##   13  15.19132  0.15219139  11.98480
##   15  15.37074  0.13197526  12.08169
##   17  15.57846  0.10567551  12.20996
##   19  15.71045  0.08705196  12.36730
##   21  15.82078  0.07375238  12.50320
##   23  15.80293  0.07362706  12.57585
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 5.

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_knn_sd<-predict(caret_knn_fit_sd,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_knn_sd<-mean((Train_D_Dataset$ACCIDENTES_LEVES-y_tr_pred_knn_sd)^2) # calcula el mse de entrenamiento
RMSE_tr_knn_sd = sqrt(mse_tr_knn_sd)
mse_tr_knn_sd
## [1] 124.3761
RMSE_tr_knn_sd
## [1] 11.1524

Calculo MSE y RMSE para los datos de validación

y_test_pred_knn_sd<-predict(caret_knn_fit_sd,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_knn_sd<-mean((Test_D_Dataset$ACCIDENTES_LEVES-y_test_pred_knn_sd)^2) # calcula el mse de entrenamiento
RMSE_test_knn_sd = sqrt(mse_test_knn_sd)
mse_test_knn_sd
## [1] 158.0779
RMSE_test_knn_sd
## [1] 12.5729

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_LEVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_knn_sd,
            name='Modelo knn',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes leves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_LEVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_knn_sd,
            name='Modelo knn',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes leves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Resumen Modelos KNN para los diferentes tipos de accidente

Tipo_de_accidentes= c("Total Accidentes","Accidentes graves","Accidentes leves")
RMSE_Train_knn = round(c(RMSE_tr_knn,RMSE_tr_knn_m,RMSE_tr_knn_sd), 3)
RMSE_Test_knn = round(c(RMSE_test_knn,RMSE_test_knn_m,RMSE_test_knn_sd),3)

Tabla_knn = data.frame (cbind(Tipo_de_accidentes,RMSE_Train_knn,RMSE_Test_knn))
Tabla_knn
##   Tipo_de_accidentes RMSE_Train_knn RMSE_Test_knn
## 1   Total Accidentes         17.492         20.91
## 2  Accidentes graves          9.814        13.416
## 3   Accidentes leves         11.152        12.573

3. MODELO LINEAL GENERALIZADO

Total Accidentes

glm_fit<-glm(TOTAL_ACCIDENTES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset, family = "poisson")
summary(glm_fit)
## 
## Call:
## glm(formula = TOTAL_ACCIDENTES ~ Ano_Base + DIA + SEMANA + Feriado_Lunes + 
##     Feriado_Otro + Madre + Semana_Santa + Viernes_Desp_Quincena_v2 + 
##     Feria_Flores, family = "poisson", data = Train_D_Dataset)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.7817  -0.9211  -0.0185   0.7671   4.7852  
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)               4.488686   0.023017 195.020  < 2e-16 ***
## Ano_Base                  0.018122   0.004971   3.646 0.000266 ***
## DIA2                     -0.002114   0.009296  -0.227 0.820100    
## DIA3                     -0.025356   0.009341  -2.715 0.006637 ** 
## DIA4                     -0.025304   0.009398  -2.692 0.007093 ** 
## DIA5                      0.018082   0.009815   1.842 0.065453 .  
## DIA6                     -0.086525   0.009484  -9.124  < 2e-16 ***
## DIA7                     -0.490167   0.010603 -46.229  < 2e-16 ***
## SEMANA02                  0.110017   0.029771   3.695 0.000219 ***
## SEMANA03                  0.272105   0.028496   9.549  < 2e-16 ***
## SEMANA04                  0.277454   0.028363   9.782  < 2e-16 ***
## SEMANA05                  0.317292   0.028142  11.275  < 2e-16 ***
## SEMANA06                  0.359992   0.027906  12.900  < 2e-16 ***
## SEMANA07                  0.378320   0.027830  13.594  < 2e-16 ***
## SEMANA08                  0.355197   0.027932  12.717  < 2e-16 ***
## SEMANA09                  0.346486   0.027999  12.375  < 2e-16 ***
## SEMANA10                  0.410986   0.027639  14.870  < 2e-16 ***
## SEMANA11                  0.395551   0.027823  14.217  < 2e-16 ***
## SEMANA12                  0.351672   0.028793  12.214  < 2e-16 ***
## SEMANA13                  0.368182   0.028165  13.072  < 2e-16 ***
## SEMANA14                  0.402735   0.028557  14.103  < 2e-16 ***
## SEMANA15                  0.394495   0.028621  13.783  < 2e-16 ***
## SEMANA16                  0.315453   0.028799  10.954  < 2e-16 ***
## SEMANA17                  0.404038   0.027706  14.583  < 2e-16 ***
## SEMANA18                  0.396121   0.027955  14.170  < 2e-16 ***
## SEMANA19                  0.368230   0.028245  13.037  < 2e-16 ***
## SEMANA20                  0.380075   0.027820  13.662  < 2e-16 ***
## SEMANA21                  0.373558   0.027926  13.377  < 2e-16 ***
## SEMANA22                  0.332104   0.028286  11.741  < 2e-16 ***
## SEMANA23                  0.392822   0.027929  14.065  < 2e-16 ***
## SEMANA24                  0.359672   0.028021  12.836  < 2e-16 ***
## SEMANA25                  0.308529   0.028396  10.865  < 2e-16 ***
## SEMANA26                  0.246334   0.028781   8.559  < 2e-16 ***
## SEMANA27                  0.337212   0.028489  11.837  < 2e-16 ***
## SEMANA28                  0.349607   0.027964  12.502  < 2e-16 ***
## SEMANA29                  0.380267   0.027975  13.593  < 2e-16 ***
## SEMANA30                  0.375028   0.028227  13.286  < 2e-16 ***
## SEMANA31                  0.411354   0.031438  13.085  < 2e-16 ***
## SEMANA32                  0.407898   0.029761  13.706  < 2e-16 ***
## SEMANA33                  0.393692   0.027843  14.140  < 2e-16 ***
## SEMANA34                  0.350120   0.028280  12.381  < 2e-16 ***
## SEMANA35                  0.383868   0.027800  13.808  < 2e-16 ***
## SEMANA36                  0.359693   0.027908  12.889  < 2e-16 ***
## SEMANA37                  0.415116   0.027639  15.019  < 2e-16 ***
## SEMANA38                  0.417499   0.027605  15.124  < 2e-16 ***
## SEMANA39                  0.343436   0.027998  12.266  < 2e-16 ***
## SEMANA40                  0.412122   0.027633  14.914  < 2e-16 ***
## SEMANA41                  0.291319   0.028284  10.300  < 2e-16 ***
## SEMANA42                  0.366934   0.028322  12.956  < 2e-16 ***
## SEMANA43                  0.361485   0.027898  12.957  < 2e-16 ***
## SEMANA44                  0.350107   0.027980  12.513  < 2e-16 ***
## SEMANA45                  0.314993   0.028618  11.007  < 2e-16 ***
## SEMANA46                  0.366429   0.028096  13.042  < 2e-16 ***
## SEMANA47                  0.354673   0.028134  12.607  < 2e-16 ***
## SEMANA48                  0.344368   0.028011  12.294  < 2e-16 ***
## SEMANA49                  0.371731   0.027972  13.289  < 2e-16 ***
## SEMANA50                  0.377630   0.027981  13.496  < 2e-16 ***
## SEMANA51                  0.415801   0.027645  15.041  < 2e-16 ***
## SEMANA52                  0.220503   0.029013   7.600 2.96e-14 ***
## SEMANA53                  0.023245   0.047561   0.489 0.625025    
## Feriado_Lunes            -0.576941   0.019813 -29.119  < 2e-16 ***
## Feriado_Otro             -0.459388   0.024470 -18.774  < 2e-16 ***
## Madre                     0.196891   0.053169   3.703 0.000213 ***
## Semana_Santa             -0.188386   0.020682  -9.109  < 2e-16 ***
## Viernes_Desp_Quincena_v2  0.029713   0.015773   1.884 0.059595 .  
## Feria_Flores              0.070515   0.021489   3.281 0.001033 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 9170.5  on 1460  degrees of freedom
## Residual deviance: 2710.5  on 1395  degrees of freedom
## AIC: 12428
## 
## Number of Fisher Scoring iterations: 4
glm_fit
## 
## Call:  glm(formula = TOTAL_ACCIDENTES ~ Ano_Base + DIA + SEMANA + Feriado_Lunes + 
##     Feriado_Otro + Madre + Semana_Santa + Viernes_Desp_Quincena_v2 + 
##     Feria_Flores, family = "poisson", data = Train_D_Dataset)
## 
## Coefficients:
##              (Intercept)                  Ano_Base  
##                 4.488686                  0.018122  
##                     DIA2                      DIA3  
##                -0.002114                 -0.025356  
##                     DIA4                      DIA5  
##                -0.025304                  0.018082  
##                     DIA6                      DIA7  
##                -0.086525                 -0.490167  
##                 SEMANA02                  SEMANA03  
##                 0.110017                  0.272105  
##                 SEMANA04                  SEMANA05  
##                 0.277454                  0.317292  
##                 SEMANA06                  SEMANA07  
##                 0.359992                  0.378320  
##                 SEMANA08                  SEMANA09  
##                 0.355197                  0.346486  
##                 SEMANA10                  SEMANA11  
##                 0.410986                  0.395551  
##                 SEMANA12                  SEMANA13  
##                 0.351672                  0.368182  
##                 SEMANA14                  SEMANA15  
##                 0.402735                  0.394495  
##                 SEMANA16                  SEMANA17  
##                 0.315453                  0.404038  
##                 SEMANA18                  SEMANA19  
##                 0.396121                  0.368230  
##                 SEMANA20                  SEMANA21  
##                 0.380075                  0.373558  
##                 SEMANA22                  SEMANA23  
##                 0.332104                  0.392822  
##                 SEMANA24                  SEMANA25  
##                 0.359672                  0.308529  
##                 SEMANA26                  SEMANA27  
##                 0.246334                  0.337212  
##                 SEMANA28                  SEMANA29  
##                 0.349607                  0.380267  
##                 SEMANA30                  SEMANA31  
##                 0.375028                  0.411354  
##                 SEMANA32                  SEMANA33  
##                 0.407898                  0.393692  
##                 SEMANA34                  SEMANA35  
##                 0.350120                  0.383868  
##                 SEMANA36                  SEMANA37  
##                 0.359693                  0.415116  
##                 SEMANA38                  SEMANA39  
##                 0.417499                  0.343436  
##                 SEMANA40                  SEMANA41  
##                 0.412122                  0.291319  
##                 SEMANA42                  SEMANA43  
##                 0.366934                  0.361485  
##                 SEMANA44                  SEMANA45  
##                 0.350107                  0.314993  
##                 SEMANA46                  SEMANA47  
##                 0.366429                  0.354673  
##                 SEMANA48                  SEMANA49  
##                 0.344368                  0.371731  
##                 SEMANA50                  SEMANA51  
##                 0.377630                  0.415801  
##                 SEMANA52                  SEMANA53  
##                 0.220503                  0.023245  
##            Feriado_Lunes              Feriado_Otro  
##                -0.576941                 -0.459388  
##                    Madre              Semana_Santa  
##                 0.196891                 -0.188386  
## Viernes_Desp_Quincena_v2              Feria_Flores  
##                 0.029713                  0.070515  
## 
## Degrees of Freedom: 1460 Total (i.e. Null);  1395 Residual
## Null Deviance:       9171 
## Residual Deviance: 2710  AIC: 12430

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_glm<-predict(glm_fit,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")],type="response")
mse_tr_glm<-mean((Train_D_Dataset$TOTAL_ACCIDENTES-y_tr_pred_glm)^2) # calcula el mse de entrenamiento
RMSE_tr_glm = sqrt(mse_tr_glm)
mse_tr_glm
## [1] 210.0413
RMSE_tr_glm
## [1] 14.4928

Calculo MSE y RMSE para los datos de validación

y_test_pred_glm<-predict(glm_fit,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")],type="response")
mse_test_glm<-mean((Train_D_Dataset$TOTAL_ACCIDENTES-y_test_pred_glm)^2) # calcula el mse de entrenamiento
## Warning in Train_D_Dataset$TOTAL_ACCIDENTES - y_test_pred_glm: longitud de
## objeto mayor no es múltiplo de la longitud de uno menor
RMSE_test_glm = sqrt(mse_test_glm)
mse_test_glm
## [1] 1126.705
RMSE_test_glm
## [1] 33.56643

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~TOTAL_ACCIDENTES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_glm,
            name='Modelo glm',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~TOTAL_ACCIDENTES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_glm,
            name='Modelo glm',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Accidentes Graves

glm_fit_m<-glm(ACCIDENTES_GRAVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset, family = "poisson")
summary(glm_fit)
## 
## Call:
## glm(formula = TOTAL_ACCIDENTES ~ Ano_Base + DIA + SEMANA + Feriado_Lunes + 
##     Feriado_Otro + Madre + Semana_Santa + Viernes_Desp_Quincena_v2 + 
##     Feria_Flores, family = "poisson", data = Train_D_Dataset)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.7817  -0.9211  -0.0185   0.7671   4.7852  
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)               4.488686   0.023017 195.020  < 2e-16 ***
## Ano_Base                  0.018122   0.004971   3.646 0.000266 ***
## DIA2                     -0.002114   0.009296  -0.227 0.820100    
## DIA3                     -0.025356   0.009341  -2.715 0.006637 ** 
## DIA4                     -0.025304   0.009398  -2.692 0.007093 ** 
## DIA5                      0.018082   0.009815   1.842 0.065453 .  
## DIA6                     -0.086525   0.009484  -9.124  < 2e-16 ***
## DIA7                     -0.490167   0.010603 -46.229  < 2e-16 ***
## SEMANA02                  0.110017   0.029771   3.695 0.000219 ***
## SEMANA03                  0.272105   0.028496   9.549  < 2e-16 ***
## SEMANA04                  0.277454   0.028363   9.782  < 2e-16 ***
## SEMANA05                  0.317292   0.028142  11.275  < 2e-16 ***
## SEMANA06                  0.359992   0.027906  12.900  < 2e-16 ***
## SEMANA07                  0.378320   0.027830  13.594  < 2e-16 ***
## SEMANA08                  0.355197   0.027932  12.717  < 2e-16 ***
## SEMANA09                  0.346486   0.027999  12.375  < 2e-16 ***
## SEMANA10                  0.410986   0.027639  14.870  < 2e-16 ***
## SEMANA11                  0.395551   0.027823  14.217  < 2e-16 ***
## SEMANA12                  0.351672   0.028793  12.214  < 2e-16 ***
## SEMANA13                  0.368182   0.028165  13.072  < 2e-16 ***
## SEMANA14                  0.402735   0.028557  14.103  < 2e-16 ***
## SEMANA15                  0.394495   0.028621  13.783  < 2e-16 ***
## SEMANA16                  0.315453   0.028799  10.954  < 2e-16 ***
## SEMANA17                  0.404038   0.027706  14.583  < 2e-16 ***
## SEMANA18                  0.396121   0.027955  14.170  < 2e-16 ***
## SEMANA19                  0.368230   0.028245  13.037  < 2e-16 ***
## SEMANA20                  0.380075   0.027820  13.662  < 2e-16 ***
## SEMANA21                  0.373558   0.027926  13.377  < 2e-16 ***
## SEMANA22                  0.332104   0.028286  11.741  < 2e-16 ***
## SEMANA23                  0.392822   0.027929  14.065  < 2e-16 ***
## SEMANA24                  0.359672   0.028021  12.836  < 2e-16 ***
## SEMANA25                  0.308529   0.028396  10.865  < 2e-16 ***
## SEMANA26                  0.246334   0.028781   8.559  < 2e-16 ***
## SEMANA27                  0.337212   0.028489  11.837  < 2e-16 ***
## SEMANA28                  0.349607   0.027964  12.502  < 2e-16 ***
## SEMANA29                  0.380267   0.027975  13.593  < 2e-16 ***
## SEMANA30                  0.375028   0.028227  13.286  < 2e-16 ***
## SEMANA31                  0.411354   0.031438  13.085  < 2e-16 ***
## SEMANA32                  0.407898   0.029761  13.706  < 2e-16 ***
## SEMANA33                  0.393692   0.027843  14.140  < 2e-16 ***
## SEMANA34                  0.350120   0.028280  12.381  < 2e-16 ***
## SEMANA35                  0.383868   0.027800  13.808  < 2e-16 ***
## SEMANA36                  0.359693   0.027908  12.889  < 2e-16 ***
## SEMANA37                  0.415116   0.027639  15.019  < 2e-16 ***
## SEMANA38                  0.417499   0.027605  15.124  < 2e-16 ***
## SEMANA39                  0.343436   0.027998  12.266  < 2e-16 ***
## SEMANA40                  0.412122   0.027633  14.914  < 2e-16 ***
## SEMANA41                  0.291319   0.028284  10.300  < 2e-16 ***
## SEMANA42                  0.366934   0.028322  12.956  < 2e-16 ***
## SEMANA43                  0.361485   0.027898  12.957  < 2e-16 ***
## SEMANA44                  0.350107   0.027980  12.513  < 2e-16 ***
## SEMANA45                  0.314993   0.028618  11.007  < 2e-16 ***
## SEMANA46                  0.366429   0.028096  13.042  < 2e-16 ***
## SEMANA47                  0.354673   0.028134  12.607  < 2e-16 ***
## SEMANA48                  0.344368   0.028011  12.294  < 2e-16 ***
## SEMANA49                  0.371731   0.027972  13.289  < 2e-16 ***
## SEMANA50                  0.377630   0.027981  13.496  < 2e-16 ***
## SEMANA51                  0.415801   0.027645  15.041  < 2e-16 ***
## SEMANA52                  0.220503   0.029013   7.600 2.96e-14 ***
## SEMANA53                  0.023245   0.047561   0.489 0.625025    
## Feriado_Lunes            -0.576941   0.019813 -29.119  < 2e-16 ***
## Feriado_Otro             -0.459388   0.024470 -18.774  < 2e-16 ***
## Madre                     0.196891   0.053169   3.703 0.000213 ***
## Semana_Santa             -0.188386   0.020682  -9.109  < 2e-16 ***
## Viernes_Desp_Quincena_v2  0.029713   0.015773   1.884 0.059595 .  
## Feria_Flores              0.070515   0.021489   3.281 0.001033 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 9170.5  on 1460  degrees of freedom
## Residual deviance: 2710.5  on 1395  degrees of freedom
## AIC: 12428
## 
## Number of Fisher Scoring iterations: 4
glm_fit_m
## 
## Call:  glm(formula = ACCIDENTES_GRAVES ~ Ano_Base + DIA + SEMANA + Feriado_Lunes + 
##     Feriado_Otro + Madre + Semana_Santa + Viernes_Desp_Quincena_v2 + 
##     Feria_Flores, family = "poisson", data = Train_D_Dataset)
## 
## Coefficients:
##              (Intercept)                  Ano_Base  
##                 3.976112                  0.006397  
##                     DIA2                      DIA3  
##                -0.017911                 -0.018039  
##                     DIA4                      DIA5  
##                -0.010024                 -0.016975  
##                     DIA6                      DIA7  
##                -0.058562                 -0.299248  
##                 SEMANA02                  SEMANA03  
##                 0.025966                  0.209462  
##                 SEMANA04                  SEMANA05  
##                 0.168745                  0.246114  
##                 SEMANA06                  SEMANA07  
##                 0.305105                  0.258630  
##                 SEMANA08                  SEMANA09  
##                 0.287786                  0.260259  
##                 SEMANA10                  SEMANA11  
##                 0.325703                  0.328136  
##                 SEMANA12                  SEMANA13  
##                 0.267391                  0.293593  
##                 SEMANA14                  SEMANA15  
##                 0.312125                  0.274098  
##                 SEMANA16                  SEMANA17  
##                 0.247631                  0.293937  
##                 SEMANA18                  SEMANA19  
##                 0.270797                  0.273891  
##                 SEMANA20                  SEMANA21  
##                 0.277996                  0.284721  
##                 SEMANA22                  SEMANA23  
##                 0.248920                  0.319613  
##                 SEMANA24                  SEMANA25  
##                 0.273177                  0.223305  
##                 SEMANA26                  SEMANA27  
##                 0.142363                  0.253616  
##                 SEMANA28                  SEMANA29  
##                 0.269599                  0.284379  
##                 SEMANA30                  SEMANA31  
##                 0.282785                  0.330713  
##                 SEMANA32                  SEMANA33  
##                 0.300057                  0.306790  
##                 SEMANA34                  SEMANA35  
##                 0.282837                  0.343966  
##                 SEMANA36                  SEMANA37  
##                 0.266924                  0.333940  
##                 SEMANA38                  SEMANA39  
##                 0.323662                  0.280236  
##                 SEMANA40                  SEMANA41  
##                 0.305625                  0.174690  
##                 SEMANA42                  SEMANA43  
##                 0.277874                  0.267465  
##                 SEMANA44                  SEMANA45  
##                 0.232769                  0.224510  
##                 SEMANA46                  SEMANA47  
##                 0.232512                  0.235860  
##                 SEMANA48                  SEMANA49  
##                 0.212498                  0.209614  
##                 SEMANA50                  SEMANA51  
##                 0.219495                  0.267641  
##                 SEMANA52                  SEMANA53  
##                 0.159871                 -0.076472  
##            Feriado_Lunes              Feriado_Otro  
##                -0.355846                 -0.270664  
##                    Madre              Semana_Santa  
##                 0.115843                 -0.178485  
## Viernes_Desp_Quincena_v2              Feria_Flores  
##                 0.044937                  0.047819  
## 
## Degrees of Freedom: 1460 Total (i.e. Null);  1395 Residual
## Null Deviance:       3880 
## Residual Deviance: 2178  AIC: 11050

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_glm_m<-predict(glm_fit_m,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")],type="response")
mse_tr_glm_m<-mean((Train_D_Dataset$ACCIDENTES_GRAVES-y_tr_pred_glm_m)^2) # calcula el mse de entrenamiento
RMSE_tr_glm_m = sqrt(mse_tr_glm_m)
mse_tr_glm_m
## [1] 95.42817
RMSE_tr_glm_m
## [1] 9.768734

Calculo MSE y RMSE para los datos de validación

y_test_pred_glm_m<-predict(glm_fit_m,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")],type="response")
mse_test_glm_m<-mean((Train_D_Dataset$ACCIDENTES_GRAVES-y_test_pred_glm_m)^2) # calcula el mse de entrenamiento
## Warning in Train_D_Dataset$ACCIDENTES_GRAVES - y_test_pred_glm_m: longitud
## de objeto mayor no es múltiplo de la longitud de uno menor
RMSE_test_glm_m = sqrt(mse_test_glm_m)
mse_test_glm_m
## [1] 216.5946
RMSE_test_glm_m
## [1] 14.71715

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_GRAVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_glm_m,
            name='Modelo glm',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes graves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes graves"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_GRAVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_glm_m,
            name='Modelo glm',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes graves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes graves"),
         legend = list(x = 0.75, y = 0.9))

Accidentes Leves

glm_fit_sd<-glm(ACCIDENTES_LEVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset, family = "poisson")
summary(glm_fit_sd)
## 
## Call:
## glm(formula = ACCIDENTES_LEVES ~ Ano_Base + DIA + SEMANA + Feriado_Lunes + 
##     Feriado_Otro + Madre + Semana_Santa + Viernes_Desp_Quincena_v2 + 
##     Feria_Flores, family = "poisson", data = Train_D_Dataset)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -6.4623  -0.8049  -0.0463   0.7879   5.7826  
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)               3.566375   0.036800  96.913  < 2e-16 ***
## Ano_Base                  0.032994   0.007481   4.411 1.03e-05 ***
## DIA2                      0.016501   0.013689   1.205  0.22804    
## DIA3                     -0.034131   0.013839  -2.466  0.01365 *  
## DIA4                     -0.043615   0.013967  -3.123  0.00179 ** 
## DIA5                      0.059498   0.014400   4.132 3.60e-05 ***
## DIA6                     -0.120702   0.014131  -8.542  < 2e-16 ***
## DIA7                     -0.779324   0.017117 -45.529  < 2e-16 ***
## SEMANA02                  0.231231   0.046740   4.947 7.53e-07 ***
## SEMANA03                  0.365275   0.044977   8.121 4.61e-16 ***
## SEMANA04                  0.429127   0.044234   9.701  < 2e-16 ***
## SEMANA05                  0.422250   0.044301   9.531  < 2e-16 ***
## SEMANA06                  0.443281   0.044121  10.047  < 2e-16 ***
## SEMANA07                  0.543252   0.043403  12.516  < 2e-16 ***
## SEMANA08                  0.455157   0.044027  10.338  < 2e-16 ***
## SEMANA09                  0.470804   0.043950  10.712  < 2e-16 ***
## SEMANA10                  0.533917   0.043425  12.295  < 2e-16 ***
## SEMANA11                  0.495529   0.043870  11.295  < 2e-16 ***
## SEMANA12                  0.473432   0.045328  10.445  < 2e-16 ***
## SEMANA13                  0.476776   0.044410  10.736  < 2e-16 ***
## SEMANA14                  0.532226   0.044751  11.893  < 2e-16 ***
## SEMANA15                  0.561218   0.044578  12.590  < 2e-16 ***
## SEMANA16                  0.415266   0.045388   9.149  < 2e-16 ***
## SEMANA17                  0.555825   0.043325  12.829  < 2e-16 ***
## SEMANA18                  0.569384   0.043655  13.043  < 2e-16 ***
## SEMANA19                  0.502320   0.044140  11.380  < 2e-16 ***
## SEMANA20                  0.524008   0.043545  12.034  < 2e-16 ***
## SEMANA21                  0.500812   0.043857  11.419  < 2e-16 ***
## SEMANA22                  0.452511   0.044516  10.165  < 2e-16 ***
## SEMANA23                  0.499732   0.044076  11.338  < 2e-16 ***
## SEMANA24                  0.484160   0.044032  10.996  < 2e-16 ***
## SEMANA25                  0.431187   0.044634   9.661  < 2e-16 ***
## SEMANA26                  0.392980   0.045004   8.732  < 2e-16 ***
## SEMANA27                  0.457483   0.044908  10.187  < 2e-16 ***
## SEMANA28                  0.465838   0.043951  10.599  < 2e-16 ***
## SEMANA29                  0.515251   0.043946  11.725  < 2e-16 ***
## SEMANA30                  0.506661   0.044204  11.462  < 2e-16 ***
## SEMANA31                  0.527088   0.048797  10.802  < 2e-16 ***
## SEMANA32                  0.559978   0.046302  12.094  < 2e-16 ***
## SEMANA33                  0.518888   0.043774  11.854  < 2e-16 ***
## SEMANA34                  0.448565   0.044720  10.031  < 2e-16 ***
## SEMANA35                  0.446606   0.044139  10.118  < 2e-16 ***
## SEMANA36                  0.491971   0.043741  11.247  < 2e-16 ***
## SEMANA37                  0.533034   0.043478  12.260  < 2e-16 ***
## SEMANA38                  0.551099   0.043299  12.728  < 2e-16 ***
## SEMANA39                  0.437909   0.044175   9.913  < 2e-16 ***
## SEMANA40                  0.561143   0.043226  12.982  < 2e-16 ***
## SEMANA41                  0.452376   0.044048  10.270  < 2e-16 ***
## SEMANA42                  0.494565   0.044608  11.087  < 2e-16 ***
## SEMANA43                  0.495312   0.043715  11.330  < 2e-16 ***
## SEMANA44                  0.512281   0.043633  11.741  < 2e-16 ***
## SEMANA45                  0.444380   0.045020   9.871  < 2e-16 ***
## SEMANA46                  0.549282   0.043752  12.554  < 2e-16 ***
## SEMANA47                  0.518930   0.043916  11.816  < 2e-16 ***
## SEMANA48                  0.523360   0.043550  12.017  < 2e-16 ***
## SEMANA49                  0.586664   0.043332  13.539  < 2e-16 ***
## SEMANA50                  0.587866   0.043386  13.550  < 2e-16 ***
## SEMANA51                  0.611326   0.042928  14.241  < 2e-16 ***
## SEMANA52                  0.308655   0.045949   6.717 1.85e-11 ***
## SEMANA53                  0.168237   0.073074   2.302  0.02132 *  
## Feriado_Lunes            -0.926258   0.034049 -27.204  < 2e-16 ***
## Feriado_Otro             -0.755488   0.041933 -18.017  < 2e-16 ***
## Madre                     0.340084   0.083848   4.056 4.99e-05 ***
## Semana_Santa             -0.200182   0.032101  -6.236 4.49e-10 ***
## Viernes_Desp_Quincena_v2  0.010337   0.023064   0.448  0.65403    
## Feria_Flores              0.098884   0.032066   3.084  0.00204 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 8499.6  on 1460  degrees of freedom
## Residual deviance: 2431.0  on 1395  degrees of freedom
## AIC: 10907
## 
## Number of Fisher Scoring iterations: 4
glm_fit_sd
## 
## Call:  glm(formula = ACCIDENTES_LEVES ~ Ano_Base + DIA + SEMANA + Feriado_Lunes + 
##     Feriado_Otro + Madre + Semana_Santa + Viernes_Desp_Quincena_v2 + 
##     Feria_Flores, family = "poisson", data = Train_D_Dataset)
## 
## Coefficients:
##              (Intercept)                  Ano_Base  
##                  3.56638                   0.03299  
##                     DIA2                      DIA3  
##                  0.01650                  -0.03413  
##                     DIA4                      DIA5  
##                 -0.04362                   0.05950  
##                     DIA6                      DIA7  
##                 -0.12070                  -0.77932  
##                 SEMANA02                  SEMANA03  
##                  0.23123                   0.36527  
##                 SEMANA04                  SEMANA05  
##                  0.42913                   0.42225  
##                 SEMANA06                  SEMANA07  
##                  0.44328                   0.54325  
##                 SEMANA08                  SEMANA09  
##                  0.45516                   0.47080  
##                 SEMANA10                  SEMANA11  
##                  0.53392                   0.49553  
##                 SEMANA12                  SEMANA13  
##                  0.47343                   0.47678  
##                 SEMANA14                  SEMANA15  
##                  0.53223                   0.56122  
##                 SEMANA16                  SEMANA17  
##                  0.41527                   0.55582  
##                 SEMANA18                  SEMANA19  
##                  0.56938                   0.50232  
##                 SEMANA20                  SEMANA21  
##                  0.52401                   0.50081  
##                 SEMANA22                  SEMANA23  
##                  0.45251                   0.49973  
##                 SEMANA24                  SEMANA25  
##                  0.48416                   0.43119  
##                 SEMANA26                  SEMANA27  
##                  0.39298                   0.45748  
##                 SEMANA28                  SEMANA29  
##                  0.46584                   0.51525  
##                 SEMANA30                  SEMANA31  
##                  0.50666                   0.52709  
##                 SEMANA32                  SEMANA33  
##                  0.55998                   0.51889  
##                 SEMANA34                  SEMANA35  
##                  0.44856                   0.44661  
##                 SEMANA36                  SEMANA37  
##                  0.49197                   0.53303  
##                 SEMANA38                  SEMANA39  
##                  0.55110                   0.43791  
##                 SEMANA40                  SEMANA41  
##                  0.56114                   0.45238  
##                 SEMANA42                  SEMANA43  
##                  0.49456                   0.49531  
##                 SEMANA44                  SEMANA45  
##                  0.51228                   0.44438  
##                 SEMANA46                  SEMANA47  
##                  0.54928                   0.51893  
##                 SEMANA48                  SEMANA49  
##                  0.52336                   0.58666  
##                 SEMANA50                  SEMANA51  
##                  0.58787                   0.61133  
##                 SEMANA52                  SEMANA53  
##                  0.30866                   0.16824  
##            Feriado_Lunes              Feriado_Otro  
##                 -0.92626                  -0.75549  
##                    Madre              Semana_Santa  
##                  0.34008                  -0.20018  
## Viernes_Desp_Quincena_v2              Feria_Flores  
##                  0.01034                   0.09888  
## 
## Degrees of Freedom: 1460 Total (i.e. Null);  1395 Residual
## Null Deviance:       8500 
## Residual Deviance: 2431  AIC: 10910

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_glm_sd<-predict(glm_fit_sd,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")],type="response")
mse_tr_glm_sd<-mean((Train_D_Dataset$ACCIDENTES_LEVES-y_tr_pred_glm_sd)^2) # calcula el mse de entrenamiento
RMSE_tr_glm_sd = sqrt(mse_tr_glm_sd)
mse_tr_glm_sd
## [1] 82.47408
RMSE_tr_glm_sd
## [1] 9.081524

Calculo MSE y RMSE para los datos de validación

y_test_pred_glm_sd<-predict(glm_fit_sd,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")],type="response")
mse_test_glm_sd<-mean((Train_D_Dataset$ACCIDENTES_LEVES-y_test_pred_glm_sd)^2) # calcula el mse de entrenamiento
## Warning in Train_D_Dataset$ACCIDENTES_LEVES - y_test_pred_glm_sd: longitud
## de objeto mayor no es múltiplo de la longitud de uno menor
RMSE_test_glm_sd = sqrt(mse_test_glm_sd)
mse_test_glm_sd
## [1] 464.2341
RMSE_test_glm_sd
## [1] 21.54609

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_LEVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_glm_sd,
            name='Modelo glm',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes leves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes leves"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_LEVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_glm_sd,
            name='Modelo glm',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes leves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes leves"),
         legend = list(x = 0.75, y = 0.9))

#### REsumen Modelos Regresión lineal generalizado para los diferentes tipos de accidente

Tipo_de_accidentes= c("Total Accidentes","Accidentes Graves","Accidentes Leves")
RMSE_Train_glm = round(c(RMSE_tr_glm,RMSE_tr_glm_m,RMSE_tr_glm_sd), 3)
RMSE_Test_glm = round(c(RMSE_test_glm,RMSE_test_glm_m,RMSE_test_glm_sd),3)

Tabla_glm = data.frame (cbind(Tipo_de_accidentes,RMSE_Train_glm,RMSE_Test_glm))
Tabla_glm
##   Tipo_de_accidentes RMSE_Train_glm RMSE_Test_glm
## 1   Total Accidentes         14.493        33.566
## 2  Accidentes Graves          9.769        14.717
## 3   Accidentes Leves          9.082        21.546

4. ARBOLES DE REGRESION

Total Accidentes

trcntrl = trainControl(method="cv", number=10)
caret_tree_fit = caret::train(TOTAL_ACCIDENTES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores,data=Train_D_Dataset,
                              method = "rpart", trControl = trcntrl,
                      parms = list(split = "gini"),
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
caret_tree_fit
## CART 
## 
## 1461 samples
##    9 predictor
## 
## Pre-processing: centered (65), scaled (65) 
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1314, 1314, 1315, 1314, 1317, 1315, ... 
## Resampling results across tuning parameters:
## 
##   cp           RMSE      Rsquared   MAE     
##   0.002809420  16.39978  0.6057978  12.59500
##   0.003426222  16.40104  0.6057009  12.61132
##   0.004088523  16.42877  0.6043830  12.62974
##   0.004296741  16.45966  0.6032039  12.66308
##   0.009015951  16.55411  0.5986592  12.71343
##   0.015577060  16.80019  0.5860808  12.89773
##   0.019519115  17.20313  0.5654403  13.20829
##   0.074166850  18.36318  0.5051650  13.77296
##   0.124936739  20.21321  0.3988816  14.92716
##   0.361935625  24.29677  0.3072139  18.73530
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was cp = 0.00280942.

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_tree<-predict(caret_tree_fit,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_tree<-mean((Train_D_Dataset$TOTAL_ACCIDENTES-y_tr_pred_tree)^2) # calcula el mse de entrenamiento
RMSE_tr_tree = sqrt(mse_tr_tree)
mse_tr_tree
## [1] 260.5989
RMSE_tr_tree
## [1] 16.14308

Calculo MSE y RMSE para los datos de validación

  y_test_pred_tree<-predict(caret_tree_fit,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_tree<-mean((Test_D_Dataset$TOTAL_ACCIDENTES-y_test_pred_tree)^2) # calcula el mse de entrenamiento
RMSE_test_tree = sqrt(mse_test_tree)
mse_test_tree
## [1] 266.3289
RMSE_test_tree
## [1] 16.31959

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~TOTAL_ACCIDENTES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_tree,
            name='Modelo tree',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~TOTAL_ACCIDENTES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_tree,
            name='Modelo tree',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Accidentes graves

trcntrl = trainControl(method="cv", number=10)
caret_tree_fit_m = caret::train(ACCIDENTES_GRAVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores,data=Train_D_Dataset,
                              method = "rpart", trControl = trcntrl,
                      parms = list(split = "gini"),
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
caret_tree_fit_m
## CART 
## 
## 1461 samples
##    9 predictor
## 
## Pre-processing: centered (65), scaled (65) 
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1315, 1315, 1314, 1314, 1314, 1317, ... 
## Resampling results across tuning parameters:
## 
##   cp           RMSE      Rsquared   MAE     
##   0.004304283  10.58815  0.3319637  8.297331
##   0.004855980  10.66021  0.3228533  8.344004
##   0.005581935  10.63185  0.3248362  8.307734
##   0.007392616  10.62566  0.3250349  8.298521
##   0.007539749  10.63250  0.3244980  8.304343
##   0.009846728  10.69329  0.3169611  8.326118
##   0.016928511  10.78241  0.3051460  8.378719
##   0.047322798  11.07134  0.2684788  8.520850
##   0.069415275  11.47112  0.2149861  8.840378
##   0.185351723  12.60088  0.1267916  9.810758
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was cp = 0.004304283.

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_tree_m<-predict(caret_tree_fit_m,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_tree_m<-mean((Train_D_Dataset$ACCIDENTES_GRAVES-y_tr_pred_tree_m)^2) # calcula el mse de entrenamiento
RMSE_tr_tree_m = sqrt(mse_tr_tree_m)
mse_tr_tree_m
## [1] 107.9408
RMSE_tr_tree_m
## [1] 10.38946

Calculo MSE y RMSE para los datos de validación

y_test_pred_tree_m<-predict(caret_tree_fit_m,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_tree_m<-mean((Test_D_Dataset$ACCIDENTES_GRAVES-y_test_pred_tree_m)^2) # calcula el mse de entrenamiento
RMSE_test_tree_m = sqrt(mse_test_tree_m)
mse_test_tree_m
## [1] 119.1989
RMSE_test_tree_m
## [1] 10.91782

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_GRAVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_tree_m,
            name='Modelo tree',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes graves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_GRAVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_tree_m,
            name='Modelo tree',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes graves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Accidentes leves

trcntrl = trainControl(method="cv", number=10)
caret_tree_fit_sd = caret::train(ACCIDENTES_LEVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores,data=Train_D_Dataset,
                              method = "rpart", trControl = trcntrl,
                      parms = list(split = "gini"),
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
caret_tree_fit_sd
## CART 
## 
## 1461 samples
##    9 predictor
## 
## Pre-processing: centered (65), scaled (65) 
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1315, 1315, 1314, 1315, 1316, 1315, ... 
## Resampling results across tuning parameters:
## 
##   cp           RMSE      Rsquared   MAE      
##   0.002017390  10.20554  0.6119329   7.930299
##   0.003376782  10.22313  0.6100858   7.968754
##   0.003692924  10.24844  0.6082667   8.007125
##   0.004346199  10.25259  0.6077759   8.012660
##   0.006899134  10.26635  0.6066895   8.024890
##   0.010072870  10.38657  0.5972172   8.101447
##   0.021713587  10.54834  0.5846967   8.241405
##   0.068542816  11.13427  0.5365761   8.578525
##   0.125802593  12.52647  0.4165720   9.419984
##   0.381699929  15.25290  0.3127356  11.939687
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was cp = 0.00201739.

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_tree_sd<-predict(caret_tree_fit_sd,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_tree_sd<-mean((Train_D_Dataset$ACCIDENTES_LEVES-y_tr_pred_tree_sd)^2) # calcula el mse de entrenamiento
RMSE_tr_tree_sd = sqrt(mse_tr_tree_sd)
mse_tr_tree_sd
## [1] 100.4275
RMSE_tr_tree_sd
## [1] 10.02135

Calculo MSE y RMSE para los datos de validación

  y_test_pred_tree_sd<-predict(caret_tree_fit_sd,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_tree_sd<-mean((Test_D_Dataset$ACCIDENTES_LEVES-y_test_pred_tree_sd)^2) # calcula el mse de entrenamiento
RMSE_test_tree_sd = sqrt(mse_test_tree_sd)
mse_test_tree_sd
## [1] 124.1118
RMSE_test_tree_sd
## [1] 11.14055

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_LEVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_tree_sd,
            name='Modelo tree',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes leves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes leves"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_LEVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_tree_sd,
            name='Modelo tree',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes leves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes leves"),
         legend = list(x = 0.75, y = 0.9))

Resumen Modelos Árboles de regresión para los diferentes tipos de accidente

Tipo_de_accidentes= c("Total Accidentes","Accidentes Graves","Accidentes Leves")
RMSE_Train_tree = round(c(RMSE_tr_tree,RMSE_tr_tree_m,RMSE_tr_tree_sd), 3)
RMSE_Test_tree = round(c(RMSE_test_tree,RMSE_test_tree_m,RMSE_test_tree_sd),3)

Tabla_tree = data.frame (cbind(Tipo_de_accidentes,RMSE_Train_tree,RMSE_Test_tree))
Tabla_tree
##   Tipo_de_accidentes RMSE_Train_tree RMSE_Test_tree
## 1   Total Accidentes          16.143          16.32
## 2  Accidentes Graves          10.389         10.918
## 3   Accidentes Leves          10.021         11.141

5. BOSQUE ALEATORIO

Total Accidentes

trcntrl = trainControl(method="cv", number=10)
caret_rf_fit = caret::train(TOTAL_ACCIDENTES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
                      method = "rf", trControl = trcntrl,
                      prox=TRUE,allowParallel=TRUE)
summary(caret_rf_fit)
##                 Length  Class      Mode     
## call                  6 -none-     call     
## type                  1 -none-     character
## predicted          1461 -none-     numeric  
## mse                 500 -none-     numeric  
## rsq                 500 -none-     numeric  
## oob.times          1461 -none-     numeric  
## importance           65 -none-     numeric  
## importanceSD          0 -none-     NULL     
## localImportance       0 -none-     NULL     
## proximity       2134521 -none-     numeric  
## ntree                 1 -none-     numeric  
## mtry                  1 -none-     numeric  
## forest               11 -none-     list     
## coefs                 0 -none-     NULL     
## y                  1461 -none-     numeric  
## test                  0 -none-     NULL     
## inbag                 0 -none-     NULL     
## xNames               65 -none-     character
## problemType           1 -none-     character
## tuneValue             1 data.frame list     
## obsLevels             1 -none-     logical  
## param                 2 -none-     list
caret_rf_fit
## Random Forest 
## 
## 1461 samples
##    9 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1314, 1315, 1317, 1315, 1314, 1315, ... 
## Resampling results across tuning parameters:
## 
##   mtry  RMSE      Rsquared   MAE     
##    2    19.71481  0.6110435  15.64526
##   33    17.34363  0.5791274  13.48688
##   65    17.62020  0.5709986  13.72791
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 33.
plot(caret_rf_fit)

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_rf<-predict(caret_rf_fit,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_rf<-mean((Train_D_Dataset$TOTAL_ACCIDENTES-y_tr_pred_rf)^2) # calcula el mse de entrenamiento
RMSE_tr_rf = sqrt(mse_tr_rf)
mse_tr_rf
## [1] 122.1749
RMSE_tr_rf
## [1] 11.05328

Calculo MSE y RMSE para los datos de validación

y_test_pred_rf<-predict(caret_rf_fit,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_rf<-mean((Test_D_Dataset$TOTAL_ACCIDENTES-y_test_pred_rf)^2) # calcula el mse de entrenamiento
RMSE_test_rf = sqrt(mse_test_rf)
mse_test_rf
## [1] 285.1705
RMSE_test_rf
## [1] 16.88699

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~TOTAL_ACCIDENTES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_rf,
            name='Modelo rf',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~TOTAL_ACCIDENTES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y=  ~y_test_pred_rf,
            name='Modelo rf',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 0.75, y = 0.9))

Accidentes Graves

trcntrl = trainControl(method="cv", number=10)
caret_rf_fit_m = caret::train(ACCIDENTES_GRAVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
                      method = "rf", trControl = trcntrl,
                      prox=TRUE,allowParallel=TRUE)
summary(caret_rf_fit_m)
##                 Length  Class      Mode     
## call                  6 -none-     call     
## type                  1 -none-     character
## predicted          1461 -none-     numeric  
## mse                 500 -none-     numeric  
## rsq                 500 -none-     numeric  
## oob.times          1461 -none-     numeric  
## importance           65 -none-     numeric  
## importanceSD          0 -none-     NULL     
## localImportance       0 -none-     NULL     
## proximity       2134521 -none-     numeric  
## ntree                 1 -none-     numeric  
## mtry                  1 -none-     numeric  
## forest               11 -none-     list     
## coefs                 0 -none-     NULL     
## y                  1461 -none-     numeric  
## test                  0 -none-     NULL     
## inbag                 0 -none-     NULL     
## xNames               65 -none-     character
## problemType           1 -none-     character
## tuneValue             1 data.frame list     
## obsLevels             1 -none-     logical  
## param                 2 -none-     list
caret_rf_fit_m
## Random Forest 
## 
## 1461 samples
##    9 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1314, 1314, 1316, 1315, 1313, 1316, ... 
## Resampling results across tuning parameters:
## 
##   mtry  RMSE      Rsquared   MAE     
##    2    11.18234  0.3499046  8.752868
##   33    11.22344  0.2942227  8.735058
##   65    11.46086  0.2818605  8.909610
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 2.
plot(caret_rf_fit_m)

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_rf_m<-predict(caret_rf_fit_m,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_rf_m<-mean((Train_D_Dataset$ACCIDENTES_GRAVES-y_tr_pred_rf_m)^2) # calcula el mse de entrenamiento
RMSE_tr_rf_m = sqrt(mse_tr_rf_m)
mse_tr_rf_m
## [1] 116.8217
RMSE_tr_rf_m
## [1] 10.80841

Calculo MSE y RMSE para los datos de validación

y_test_pred_rf_m<-predict(caret_rf_fit_m,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_rf_m<-mean((Test_D_Dataset$ACCIDENTES_GRAVES-y_test_pred_rf_m)^2) # calcula el mse de entrenamiento
RMSE_test_rf_m = sqrt(mse_test_rf_m)
mse_test_rf_m
## [1] 144.8967
RMSE_test_rf_m
## [1] 12.03731

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_GRAVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_rf_m,
            name='Modelo rf',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes graves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes graves"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_GRAVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_rf_m,
            name='Modelo rf',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes graves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes graves"),
         legend = list(x = 0.75, y = 0.9))

Accidentes Leves

trcntrl = trainControl(method="cv", number=10)
caret_rf_fit_sd = caret::train(ACCIDENTES_LEVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
                      method = "rf", trControl = trcntrl,
                      prox=TRUE,allowParallel=TRUE)
summary(caret_rf_fit_sd)
##                 Length  Class      Mode     
## call                  6 -none-     call     
## type                  1 -none-     character
## predicted          1461 -none-     numeric  
## mse                 500 -none-     numeric  
## rsq                 500 -none-     numeric  
## oob.times          1461 -none-     numeric  
## importance           65 -none-     numeric  
## importanceSD          0 -none-     NULL     
## localImportance       0 -none-     NULL     
## proximity       2134521 -none-     numeric  
## ntree                 1 -none-     numeric  
## mtry                  1 -none-     numeric  
## forest               11 -none-     list     
## coefs                 0 -none-     NULL     
## y                  1461 -none-     numeric  
## test                  0 -none-     NULL     
## inbag                 0 -none-     NULL     
## xNames               65 -none-     character
## problemType           1 -none-     character
## tuneValue             1 data.frame list     
## obsLevels             1 -none-     logical  
## param                 2 -none-     list
caret_rf_fit_sd
## Random Forest 
## 
## 1461 samples
##    9 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1316, 1313, 1314, 1314, 1317, 1315, ... 
## Resampling results across tuning parameters:
## 
##   mtry  RMSE      Rsquared   MAE      
##    2    12.37653  0.6118432  10.061618
##   33    10.98192  0.5694633   8.603879
##   65    11.17931  0.5596107   8.761452
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 33.
plot(caret_rf_fit_sd)

Calculo MSE y RMSE para los datos de entrenamiento

y_tr_pred_rf_sd<-predict(caret_rf_fit_sd,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_rf_sd<-mean((Train_D_Dataset$ACCIDENTES_LEVES-y_tr_pred_rf_sd)^2) # calcula el mse de entrenamiento
RMSE_tr_rf_sd = sqrt(mse_tr_rf_sd)
mse_tr_rf_sd
## [1] 48.48793
RMSE_tr_rf_sd
## [1] 6.963327

Calculo MSE y RMSE para los datos de validación

y_test_pred_rf_sd<-predict(caret_rf_fit_sd,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_rf_sd<-mean((Test_D_Dataset$ACCIDENTES_LEVES-y_test_pred_rf_sd)^2) # calcula el mse de entrenamiento
RMSE_test_rf_sd = sqrt(mse_test_rf_sd)
mse_test_rf_sd
## [1] 125.306
RMSE_test_rf_sd
## [1] 11.19401

Predicción en la muestra

plot_ly (data=Train_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_LEVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_tr_pred_rf_sd,
            name='Modelo rf',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes leves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes leves"),
         legend = list(x = 0.75, y = 0.9))

Gráfica serie 2018

plot_ly (data=Test_D_Dataset,
         x = ~FECHA,
         y = ~ACCIDENTES_LEVES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~y_test_pred_rf_sd,
            name='Modelo rf',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  layout(title='Total accidentes leves',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes leves"),
         legend = list(x = 0.75, y = 0.9))

Resumen Modelos Random Forest para los diferentes tipos de accidente

Tipo_de_accidentes= c("Total Accidentes","Total Graves","Total Leves")
RMSE_Train_rf = round(c(RMSE_tr_rf,RMSE_tr_rf_m,RMSE_tr_rf_sd), 3)
RMSE_Test_rf = round(c(RMSE_test_rf,RMSE_test_rf_m,RMSE_test_rf_sd),3)

Tabla_rf = data.frame (cbind(Tipo_de_accidentes,RMSE_Train_rf,RMSE_Test_rf))
Tabla_rf
##   Tipo_de_accidentes RMSE_Train_rf RMSE_Test_rf
## 1   Total Accidentes        11.053       16.887
## 2       Total Graves        10.808       12.037
## 3        Total Leves         6.963       11.194

ELECCION DEL MODELO

1. Elección del modelo para el total de accidentes

Comparación en el entrenamiento

comparacion_tr<-data.frame(FECHA=Total_Dataset_Freq$FECHA[Total_Dataset_Freq$FECHA<="2017-12-31"],
                          ACCIDENTES=Total_Dataset_Freq$TOTAL_ACCIDENTES[Total_Dataset_Freq$FECHA<="2017-12-31"],
                           lm= y_tr_pred_lm, 
                           knn= y_tr_pred_knn, 
                           glm=y_tr_pred_glm ,
                           arbol=y_tr_pred_tree,
                           rf=y_tr_pred_rf)
plot_ly (data=comparacion_tr,
         x = ~FECHA,
         y = ~ACCIDENTES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~lm,
            name='lm',
            line=list(width=1,color= "blue"))%>%
  add_trace(y= ~knn,
            name='knn',
            line=list(width=1,color="red"))%>%
  add_trace(y= ~glm,
            name='Modelo Poisson',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  add_trace(y= ~arbol,
            name='Árbol',
            line=list(width=1,color="green"))%>%
    add_trace(y= ~rf,
            name='Bosque',
            line=list(width=1,color='rgb(255, 51, 153)'))%>%
  layout(title='Total accidentes (Entrenamiento)',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 1, y = 0.9))

Comparación en la validación

comparacion_vl<-data.frame(FECHA=Test_D_Dataset$FECHA,
                          ACCIDENTES=Test_D_Dataset$TOTAL_ACCIDENTES,
                           lm= y_test_pred_lm, 
                           knn= y_test_pred_knn, 
                           glm=y_test_pred_glm ,
                           arbol=y_test_pred_tree,
                           rf=y_test_pred_rf)
plot_ly (data=comparacion_vl,
         x = ~FECHA,
         y = ~ACCIDENTES,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~lm,
            name='lm',
            line=list(width=1,color= "blue"))%>%
  add_trace(y= ~knn,
            name='knn',
            line=list(width=1,color="red"))%>%
  add_trace(y= ~glm,
            name='Modelo Poisson',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  add_trace(y= ~arbol,
            name='Árbol',
            line=list(width=1,color="green"))%>%
  add_trace(y= ~rf,
            name='Bosque',
            line=list(width=1,color='rgb(255, 51, 153)'))%>%
  layout(title='Total Accidentes (Validación)',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 1, y = 0.9))

Comparación con los RMSE:

Entrenamiento<-round(c(RMSE_tr_lm,RMSE_tr_knn,RMSE_tr_glm,RMSE_tr_tree,RMSE_tr_rf),3) 
Validacion<-round(c(RMSE_test_lm,RMSE_test_knn,RMSE_test_glm,RMSE_test_tree,RMSE_test_rf),3) 
nombres<-c("lm","knn","glm","árbol","bosque")
ResultadosRMSE<-data.frame(Entrenamiento=Entrenamiento,Validacion=Validacion)
rownames(ResultadosRMSE)<-nombres

Cálculo de la variación

ResultadosRMSE$Por_variacion<-((ResultadosRMSE$Validacion-ResultadosRMSE$Entrenamiento)/ResultadosRMSE$Entrenamiento)*100
ResultadosRMSE
##        Entrenamiento Validacion Por_variacion
## lm            14.701     16.026      9.012992
## knn           17.492     20.910     19.540361
## glm           14.493     33.566    131.601463
## árbol         16.143     16.320      1.096450
## bosque        11.053     16.887     52.782050

2. Elección del modelo para Accidentes graves

Comparación en el entrenamiento

comparacion_tr<-data.frame(FECHA=Total_Dataset_Freq$FECHA[Total_Dataset_Freq$FECHA<="2017-12-31"],
                          ACCIDENTESG=Total_Dataset_Freq$ACCIDENTES_GRAVES[Total_Dataset_Freq$FECHA<="2017-12-31"],
                           lm= y_tr_pred_lm_m, 
                           knn= y_tr_pred_knn_m, 
                           glm=y_tr_pred_glm_m,
                           arbol=y_tr_pred_tree_m,
                           rf=y_tr_pred_rf_m)
plot_ly (data=comparacion_tr,
         x = ~FECHA,
         y = ~ACCIDENTESG,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~lm,
            name='lm',
            line=list(width=1,color= "blue"))%>%
  add_trace(y= ~knn,
            name='knn',
            line=list(width=1,color="red"))%>%
  add_trace(y= ~glm,
            name='Modelo Poisson',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  add_trace(y= ~arbol,
            name='Árbo',
            line=list(width=1,color="green"))%>%
    add_trace(y= ~rf,
            name='Bosque',
            line=list(width=1,color='rgb(255, 51, 153)'))%>%
  layout(title='Accidentes graves (Entrenamiento)',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 1, y = 0.9))

Comparación en la validación

comparacion_vl<-data.frame(FECHA=Test_D_Dataset$FECHA,
                          ACCIDENTESG=Test_D_Dataset$ACCIDENTES_GRAVES,
                           lm= y_test_pred_lm_m, 
                           knn= y_test_pred_knn_m, 
                           glm=y_test_pred_glm_m,
                           arbol=y_test_pred_tree_m,
                           rf=y_test_pred_rf_m)
plot_ly (data=comparacion_vl,
         x = ~FECHA,
         y = ~ACCIDENTESG,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~lm,
            name='lm',
            line=list(width=1,color= "blue"))%>%
  add_trace(y= ~knn,
            name='knn',
            line=list(width=1,color="red"))%>%
  add_trace(y= ~glm,
            name='Modelo Poisson',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  add_trace(y= ~arbol,
            name='Árbol',
            line=list(width=1,color="green"))%>%
  add_trace(y= ~rf,
            name='Bosque',
            line=list(width=1,color='rgb(255, 51, 153)'))%>%
  layout(title='Accidentes graves (Validación)',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 1, y = 0.9))

Comparación con los RMSE:

Entrenamiento<-round(c(RMSE_tr_lm_m,RMSE_tr_knn_m,RMSE_tr_glm_m,RMSE_tr_tree_m,RMSE_tr_rf_m),3) 
Validacion<-round(c(RMSE_test_lm_m,RMSE_test_knn_m,RMSE_test_glm_m,RMSE_test_tree_m,RMSE_test_rf_m),3) 
nombres<-c("lm","knn","glm","árbol","bosque")
ResultadosRMSE<-data.frame(Entrenamiento=Entrenamiento,Validacion=Validacion)
rownames(ResultadosRMSE)<-nombres

Cálculo de la variación

ResultadosRMSE$Por_variacion<-((ResultadosRMSE$Validacion-ResultadosRMSE$Entrenamiento)/ResultadosRMSE$Entrenamiento)*100
ResultadosRMSE
##        Entrenamiento Validacion Por_variacion
## lm             9.816     11.337     15.495110
## knn            9.814     13.416     36.702670
## glm            9.769     14.717     50.650015
## árbol         10.389     10.918      5.091924
## bosque        10.808     12.037     11.371207

3. Elección del modelo para Accidentes leves

Comparación en el entrenamiento

comparacion_tr<-data.frame(FECHA=Total_Dataset_Freq$FECHA[Total_Dataset_Freq$FECHA<="2017-12-31"],
                          ACCIDENTESL=Total_Dataset_Freq$ACCIDENTES_LEVES[Total_Dataset_Freq$FECHA<="2017-12-31"],
                           lm= y_tr_pred_lm_sd, 
                           knn= y_tr_pred_knn_sd, 
                           glm=y_tr_pred_glm_sd,
                           arbol=y_tr_pred_tree_sd,
                           rf=y_tr_pred_rf_sd)
plot_ly (data=comparacion_tr,
         x = ~FECHA,
         y = ~ACCIDENTESL,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~lm,
            name='lm',
            line=list(width=1,color= "blue"))%>%
  add_trace(y= ~knn,
            name='knn',
            line=list(width=1,color="red"))%>%
  add_trace(y= ~glm,
            name='Modelo Poisson',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  add_trace(y= ~arbol,
            name='Árbo',
            line=list(width=1,color="green"))%>%
    add_trace(y= ~rf,
            name='Bosque',
            line=list(width=1,color='rgb(255, 51, 153)'))%>%
  layout(title='Accidentes leves (Entrenamiento)',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 1, y = 0.9))

Comparación en la validación

comparacion_vl<-data.frame(FECHA=Test_D_Dataset$FECHA,
                          ACCIDENTESL=Test_D_Dataset$ACCIDENTES_GRAVES,
                           lm= y_test_pred_lm_sd, 
                           knn= y_test_pred_knn_sd, 
                           glm=y_test_pred_glm_sd,
                           arbol=y_test_pred_tree_sd,
                           rf=y_test_pred_rf_sd)
plot_ly (data=comparacion_vl,
         x = ~FECHA,
         y = ~ACCIDENTESL,
         type = "scatter" ,mode = "lines",
         name='Real',
         line=list(width=1,color='rgb(205, 12, 24)'))%>%
  add_trace(y= ~lm,
            name='lm',
            line=list(width=1,color= "blue"))%>%
  add_trace(y= ~knn,
            name='knn',
            line=list(width=1,color="red"))%>%
  add_trace(y= ~glm,
            name='Modelo Poisson',
            line=list(width=1,color='rgb(22, 96, 167)'))%>%
  add_trace(y= ~arbol,
            name='Árbol',
            line=list(width=1,color="green"))%>%
  add_trace(y= ~rf,
            name='Bosque',
            line=list(width=1,color='rgb(255, 51, 153)'))%>%
  layout(title='Accidentes leves (Validación)',
         xaxis=list(title="Fecha"),
         yaxis=list(title="Accidentes"),
         legend = list(x = 1, y = 0.9))

Comparación con los RMSE:

Entrenamiento<-round(c(RMSE_tr_lm_sd,RMSE_tr_knn_sd,RMSE_tr_glm_sd,RMSE_tr_tree_sd,RMSE_tr_rf_sd),3) 
Validacion<-round(c(RMSE_test_lm_sd,RMSE_test_knn_sd,RMSE_test_glm_sd,RMSE_test_tree_sd,RMSE_test_rf_sd),3)
nombres<-c("lm","knn","glm","árbol","bosque")
ResultadosRMSE<-data.frame(Entrenamiento=Entrenamiento,Validacion=Validacion)
rownames(ResultadosRMSE)<-nombres

Cálculo de la variación

ResultadosRMSE$Por_variacion<-((ResultadosRMSE$Validacion-ResultadosRMSE$Entrenamiento)/ResultadosRMSE$Entrenamiento)*100
ResultadosRMSE
##        Entrenamiento Validacion Por_variacion
## lm             9.237     10.412      12.72058
## knn           11.152     12.573      12.74211
## glm            9.082     21.546     137.23849
## árbol         10.021     11.141      11.17653
## bosque         6.963     11.194      60.76404

Modelos elegidos

Teniendo como criterio el mínimo RMSE y que la variación entre los datos de entrenamiento y validación no superen el 15 %, se eligieron los siguientes modelos:

Modelo de regresión lineal para predición de Total Accidentes

Se ajusta el modelo con todos los datos desde el 01-01-2014 al 31-12-2018

library(caret)
trcntrl = trainControl(method="cv", number=10)
caret_lm_fit_final = caret::train(TOTAL_ACCIDENTES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Total_Dataset_Freq,
                      method = "lm", trControl = trcntrl,
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
summary(caret_lm_fit_final)
## 
## Call:
## lm(formula = .outcome ~ ., data = dat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -73.865  -9.721  -0.363   9.032  70.719 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              114.69113    0.35332 324.614  < 2e-16 ***
## Ano_Base                   0.08121    0.35916   0.226   0.8211    
## DIA2                      -0.17524    0.49485  -0.354   0.7233    
## DIA3                      -1.00888    0.49500  -2.038   0.0417 *  
## DIA4                      -1.22787    0.49680  -2.472   0.0135 *  
## DIA5                       0.95405    0.53120   1.796   0.0727 .  
## DIA6                      -3.50731    0.49564  -7.076 2.13e-12 ***
## DIA7                     -17.29256    0.49822 -34.708  < 2e-16 ***
## SEMANA02                   1.05373    0.49996   2.108   0.0352 *  
## SEMANA03                   3.50386    0.49925   7.018 3.20e-12 ***
## SEMANA04                   3.33331    0.49931   6.676 3.28e-11 ***
## SEMANA05                   3.82910    0.49933   7.668 2.86e-14 ***
## SEMANA06                   4.50106    0.49931   9.015  < 2e-16 ***
## SEMANA07                   5.18987    0.49959  10.388  < 2e-16 ***
## SEMANA08                   4.57160    0.49931   9.156  < 2e-16 ***
## SEMANA09                   4.37872    0.49959   8.765  < 2e-16 ***
## SEMANA10                   5.15547    0.49931  10.325  < 2e-16 ***
## SEMANA11                   5.20133    0.50026  10.397  < 2e-16 ***
## SEMANA12                   4.65194    0.50783   9.160  < 2e-16 ***
## SEMANA13                   4.67356    0.50766   9.206  < 2e-16 ***
## SEMANA14                   5.21722    0.50753  10.280  < 2e-16 ***
## SEMANA15                   4.96149    0.50756   9.775  < 2e-16 ***
## SEMANA16                   3.97923    0.50408   7.894 5.10e-15 ***
## SEMANA17                   5.21980    0.49893  10.462  < 2e-16 ***
## SEMANA18                   4.93102    0.49965   9.869  < 2e-16 ***
## SEMANA19                   4.78638    0.51173   9.353  < 2e-16 ***
## SEMANA20                   4.88423    0.49950   9.778  < 2e-16 ***
## SEMANA21                   4.95955    0.49923   9.934  < 2e-16 ***
## SEMANA22                   4.24158    0.49960   8.490  < 2e-16 ***
## SEMANA23                   4.96348    0.49959   9.935  < 2e-16 ***
## SEMANA24                   4.66871    0.49960   9.345  < 2e-16 ***
## SEMANA25                   3.92896    0.49932   7.869 6.21e-15 ***
## SEMANA26                   2.85730    0.49934   5.722 1.23e-08 ***
## SEMANA27                   3.96033    0.50064   7.910 4.49e-15 ***
## SEMANA28                   4.30223    0.49924   8.618  < 2e-16 ***
## SEMANA29                   5.04255    0.49929  10.099  < 2e-16 ***
## SEMANA30                   4.83777    0.50544   9.571  < 2e-16 ***
## SEMANA31                   5.44505    0.58108   9.371  < 2e-16 ***
## SEMANA32                   5.10445    0.56751   8.994  < 2e-16 ***
## SEMANA33                   5.21339    0.49950  10.437  < 2e-16 ***
## SEMANA34                   4.53636    0.50003   9.072  < 2e-16 ***
## SEMANA35                   4.88814    0.49959   9.784  < 2e-16 ***
## SEMANA36                   4.74401    0.49931   9.501  < 2e-16 ***
## SEMANA37                   5.32209    0.49933  10.658  < 2e-16 ***
## SEMANA38                   5.37099    0.49931  10.757  < 2e-16 ***
## SEMANA39                   4.56086    0.49924   9.136  < 2e-16 ***
## SEMANA40                   5.35633    0.49924  10.729  < 2e-16 ***
## SEMANA41                   3.62721    0.49931   7.265 5.60e-13 ***
## SEMANA42                   4.55408    0.50066   9.096  < 2e-16 ***
## SEMANA43                   4.52849    0.49931   9.070  < 2e-16 ***
## SEMANA44                   4.85287    0.49959   9.714  < 2e-16 ***
## SEMANA45                   3.88980    0.50064   7.770 1.33e-14 ***
## SEMANA46                   4.79411    0.49986   9.591  < 2e-16 ***
## SEMANA47                   4.55202    0.49932   9.116  < 2e-16 ***
## SEMANA48                   4.01821    0.49959   8.043 1.59e-15 ***
## SEMANA49                   4.94092    0.49891   9.904  < 2e-16 ***
## SEMANA50                   4.96321    0.49888   9.949  < 2e-16 ***
## SEMANA51                   5.46667    0.49893  10.957  < 2e-16 ***
## SEMANA52                   2.63841    0.49916   5.286 1.41e-07 ***
## SEMANA53                   0.09892    0.38818   0.255   0.7989    
## Feriado_Lunes             -9.23249    0.40698 -22.685  < 2e-16 ***
## Feriado_Otro              -6.00574    0.37856 -15.865  < 2e-16 ***
## Madre                      0.94338    0.37489   2.516   0.0119 *  
## Semana_Santa              -2.78568    0.41266  -6.750 1.99e-11 ***
## Viernes_Desp_Quincena_v2   0.31527    0.41259   0.764   0.4449    
## Feria_Flores               1.30801    0.53772   2.433   0.0151 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.1 on 1760 degrees of freedom
## Multiple R-squared:  0.6766, Adjusted R-squared:  0.6646 
## F-statistic: 56.64 on 65 and 1760 DF,  p-value: < 2.2e-16

Se guardan el modelo en un objeto de r

saveRDS(caret_lm_fit_final,"../Modelos/Prediccion_Total_Diario.rds")
Modelo_Total_diario<-readRDS(file="../Modelos/Prediccion_Total_Diario.rds")
Modelo de árbol de regresión para predición de Accidentes Graves

Se ajusta el modelo con todos los datos desde el 01-01-2014 al 31-12-2018

trcntrl = trainControl(method="cv", number=10)
caret_tree_fit_m_final = caret::train(ACCIDENTES_GRAVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores,data=Total_Dataset_Freq,
                              method = "rpart", trControl = trcntrl,
                      parms = list(split = "gini"),
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
caret_tree_fit_m_final
## CART 
## 
## 1826 samples
##    9 predictor
## 
## Pre-processing: centered (65), scaled (65) 
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 1643, 1645, 1643, 1642, 1643, 1643, ... 
## Resampling results across tuning parameters:
## 
##   cp           RMSE      Rsquared   MAE     
##   0.004063053  10.53984  0.3444020  8.256158
##   0.004658364  10.57656  0.3397463  8.285625
##   0.004969618  10.59364  0.3370691  8.296671
##   0.005631103  10.57242  0.3393593  8.266976
##   0.008619910  10.67194  0.3275812  8.351267
##   0.009217134  10.69428  0.3252437  8.355144
##   0.015452952  10.79810  0.3121552  8.442178
##   0.049148474  11.09790  0.2749346  8.590807
##   0.067434570  11.50460  0.2194453  8.899255
##   0.194454760  12.58149  0.1586137  9.804889
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was cp = 0.004063053.

Se guardan el modelo en un objeto de r

saveRDS(caret_tree_fit_m_final,"../Modelos/Prediccion_Grave_Diario.rds")
Modelo_Grave_diario<-readRDS(file="../Modelos/Prediccion_Grave_Diario.rds")
Modelo de regresión lineal para predición de Accidentes Leves

Se ajusta el modelo con todos los datos desde el 01-01-2014 al 31-12-2018

trcntrl = trainControl(method="cv", number=10)
caret_lm_fit_sd_final = caret::train(ACCIDENTES_LEVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Total_Dataset_Freq,
                      method = "lm", trControl = trcntrl,
                      preProcess=c("center", "scale"),
                      tuneLength = 10)
summary(caret_lm_fit_sd_final)
## 
## Call:
## lm(formula = .outcome ~ ., data = dat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -58.228  -5.797  -0.440   5.919  40.506 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               51.4639     0.2248 228.923  < 2e-16 ***
## Ano_Base                   0.8777     0.2285   3.841 0.000127 ***
## DIA2                       0.3710     0.3149   1.178 0.238789    
## DIA3                      -0.5645     0.3150  -1.792 0.073275 .  
## DIA4                      -0.9083     0.3161  -2.874 0.004107 ** 
## DIA5                       1.3032     0.3380   3.856 0.000120 ***
## DIA6                      -2.0862     0.3154  -6.615 4.91e-11 ***
## DIA7                     -10.8172     0.3170 -34.122  < 2e-16 ***
## SEMANA02                   0.9910     0.3181   3.115 0.001868 ** 
## SEMANA03                   1.9222     0.3177   6.051 1.76e-09 ***
## SEMANA04                   2.0861     0.3177   6.566 6.77e-11 ***
## SEMANA05                   2.2631     0.3177   7.123 1.53e-12 ***
## SEMANA06                   2.3526     0.3177   7.405 2.02e-13 ***
## SEMANA07                   3.1001     0.3179   9.752  < 2e-16 ***
## SEMANA08                   2.5250     0.3177   7.948 3.36e-15 ***
## SEMANA09                   2.5084     0.3179   7.891 5.22e-15 ***
## SEMANA10                   2.6935     0.3177   8.478  < 2e-16 ***
## SEMANA11                   2.6438     0.3183   8.306  < 2e-16 ***
## SEMANA12                   2.6468     0.3231   8.191 4.91e-16 ***
## SEMANA13                   2.4947     0.3230   7.723 1.89e-14 ***
## SEMANA14                   2.9667     0.3229   9.187  < 2e-16 ***
## SEMANA15                   2.9330     0.3230   9.082  < 2e-16 ***
## SEMANA16                   2.2395     0.3207   6.982 4.10e-12 ***
## SEMANA17                   3.0787     0.3175   9.698  < 2e-16 ***
## SEMANA18                   2.8915     0.3179   9.095  < 2e-16 ***
## SEMANA19                   2.8266     0.3256   8.681  < 2e-16 ***
## SEMANA20                   3.0021     0.3178   9.446  < 2e-16 ***
## SEMANA21                   2.7718     0.3176   8.726  < 2e-16 ***
## SEMANA22                   2.3476     0.3179   7.385 2.34e-13 ***
## SEMANA23                   2.6620     0.3179   8.374  < 2e-16 ***
## SEMANA24                   2.5749     0.3179   8.100 1.02e-15 ***
## SEMANA25                   2.2623     0.3177   7.121 1.56e-12 ***
## SEMANA26                   1.8594     0.3177   5.852 5.77e-09 ***
## SEMANA27                   2.2308     0.3185   7.003 3.55e-12 ***
## SEMANA28                   2.4724     0.3177   7.783 1.20e-14 ***
## SEMANA29                   2.9064     0.3177   9.149  < 2e-16 ***
## SEMANA30                   2.6409     0.3216   8.212 4.17e-16 ***
## SEMANA31                   2.9031     0.3697   7.852 7.07e-15 ***
## SEMANA32                   2.9185     0.3611   8.082 1.17e-15 ***
## SEMANA33                   2.9080     0.3178   9.150  < 2e-16 ***
## SEMANA34                   2.5326     0.3182   7.960 3.05e-15 ***
## SEMANA35                   2.4183     0.3179   7.607 4.53e-14 ***
## SEMANA36                   2.6661     0.3177   8.392  < 2e-16 ***
## SEMANA37                   2.9097     0.3177   9.158  < 2e-16 ***
## SEMANA38                   3.0697     0.3177   9.662  < 2e-16 ***
## SEMANA39                   2.5430     0.3177   8.005 2.14e-15 ***
## SEMANA40                   3.1543     0.3177   9.930  < 2e-16 ***
## SEMANA41                   2.4153     0.3177   7.602 4.70e-14 ***
## SEMANA42                   2.7135     0.3186   8.518  < 2e-16 ***
## SEMANA43                   2.7366     0.3177   8.614  < 2e-16 ***
## SEMANA44                   2.9434     0.3179   9.259  < 2e-16 ***
## SEMANA45                   2.5678     0.3185   8.061 1.38e-15 ***
## SEMANA46                   3.3625     0.3180  10.572  < 2e-16 ***
## SEMANA47                   2.9089     0.3177   9.156  < 2e-16 ***
## SEMANA48                   2.6181     0.3179   8.236 3.43e-16 ***
## SEMANA49                   3.4415     0.3175  10.841  < 2e-16 ***
## SEMANA50                   3.3889     0.3174  10.676  < 2e-16 ***
## SEMANA51                   3.5215     0.3175  11.093  < 2e-16 ***
## SEMANA52                   1.6008     0.3176   5.040 5.13e-07 ***
## SEMANA53                   0.3206     0.2470   1.298 0.194513    
## Feriado_Lunes             -5.8103     0.2590 -22.437  < 2e-16 ***
## Feriado_Otro              -3.8256     0.2409 -15.883  < 2e-16 ***
## Madre                      0.6015     0.2385   2.522 0.011773 *  
## Semana_Santa              -1.2844     0.2626  -4.892 1.09e-06 ***
## Viernes_Desp_Quincena_v2   0.0748     0.2625   0.285 0.775722    
## Feria_Flores               0.9906     0.3421   2.895 0.003835 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.606 on 1760 degrees of freedom
## Multiple R-squared:  0.6705, Adjusted R-squared:  0.6583 
## F-statistic:  55.1 on 65 and 1760 DF,  p-value: < 2.2e-16

Se guardan el modelo en un objeto de r

saveRDS(caret_lm_fit_sd_final,"../Modelos/Prediccion_leves_Diario.rds")
Modelo_leves_diario<-readRDS(file="../Modelos/Prediccion_leves_Diario.rds")
Datos para pronóstico

Se oganizan los datos necesarios para el pronóstico de los accidentes en los años 2019, 2020 y 2021

Importación de los datos

load("../data/Dias_Especiales_Diario.Rda")
Dias_Especiales$DIA <-as.factor(format(Dias_Especiales$Fecha,'%u'))
head(Dias_Especiales)
##        Fecha Ano_Base Lunes martes miercoles jueves viernes sabado domingo
## 1 2014-01-01        0     0      0         1      0       0      0       0
## 2 2014-01-02        0     0      0         0      1       0      0       0
## 3 2014-01-03        0     0      0         0      0       1      0       0
## 4 2014-01-04        0     0      0         0      0       0      1       0
## 5 2014-01-05        0     0      0         0      0       0      0       1
## 6 2014-01-06        0     1      0         0      0       0      0       0
##   Enero Febrero Marzo Abril Mayo Junio Julio Agosto Septiembre Octubre
## 1     1       0     0     0    0     0     0      0          0       0
## 2     1       0     0     0    0     0     0      0          0       0
## 3     1       0     0     0    0     0     0      0          0       0
## 4     1       0     0     0    0     0     0      0          0       0
## 5     1       0     0     0    0     0     0      0          0       0
## 6     1       0     0     0    0     0     0      0          0       0
##   Noviembre Diciembre Feriado Feriado_v1 Feriado_Lunes Feriado_Otro
## 1         0         0       1          1             0            1
## 2         0         0       0          0             0            0
## 3         0         0       0          0             0            0
## 4         0         0       0          0             0            0
## 5         0         0       0          0             0            0
## 6         0         0       1          1             1            0
##   Previo_feriado Semana_Santa Semana_Santa_Mes Semana_Santa_Semana Prima
## 1              0            0                0                   0     0
## 2              0            0                0                   0     0
## 3              1            0                0                   0     0
## 4              1            0                0                   0     0
## 5              1            0                0                   0     0
## 6              0            0                0                   0     0
##   Mujer Padre Madre AmoryAmistad Semana_Santa_v1 Viernes_Antes_Puente
## 1     0     0     0            0               0                    0
## 2     0     0     0            0               0                    0
## 3     0     0     0            0               0                    1
## 4     0     0     0            0               0                    0
## 5     0     0     0            0               0                    0
## 6     0     0     0            0               0                    0
##   Quincena Viernes_Desp_Quincena Viernes_Desp_Quincena_v1
## 1        0                     0                        0
## 2        0                     0                        0
## 3        0                     0                        0
## 4        0                     0                        0
## 5        0                     0                        0
## 6        0                     0                        0
##   Viernes_Desp_Quincena_v2 Feria_Flores Feria_Flores_Mes
## 1                        0            0                0
## 2                        0            0                0
## 3                        0            0                0
## 4                        0            0                0
## 5                        0            0                0
## 6                        0            0                0
##   Feria_Flores_Semana  ANO SEMANA MES DIA
## 1                   0 2014     01  01   3
## 2                   0 2014     01  01   4
## 3                   0 2014     01  01   5
## 4                   0 2014     01  01   6
## 5                   0 2014     01  01   7
## 6                   0 2014     02  01   1
datos_pronostico_diario<-Dias_Especiales[,c("Fecha","Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")]

Predicción del Total de accidentes con el modelo de regresión lineal

datos_pronostico_diario$prediccion_Total<-predict(Modelo_Total_diario,datos_pronostico_diario[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])

Predicción de accidentes graves con el modelo de árbol de regresión

datos_pronostico_diario$prediccion_Graves<-predict(Modelo_Grave_diario,datos_pronostico_diario[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])

Predicción de accidentes leves con el modelo de regresión lineal

datos_pronostico_diario$prediccion_Leves<-predict(Modelo_leves_diario,datos_pronostico_diario[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])

Se guardan los datos de pronóstico en un objeto de r

save(datos_pronostico_diario,file="../Modelos/datos_pronostico_diario.Rda")